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Loni, Mohammad, PhD Candidate
Publications (10 of 26) Show all publications
Asadi, M., Poursalim, F., Loni, M., Daneshtalab, M., Sjödin, M. & Gharehbaghi, A. (2023). Accurate detection of paroxysmal atrial fibrillation with certified-GAN and neural architecture search. Scientific Reports, 13(1)
Open this publication in new window or tab >>Accurate detection of paroxysmal atrial fibrillation with certified-GAN and neural architecture search
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2023 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 13, no 1Article in journal (Refereed) Published
Abstract [en]

This paper presents a novel machine learning framework for detecting PxAF, a pathological characteristic of electrocardiogram (ECG) that can lead to fatal conditions such as heart attack. To enhance the learning process, the framework involves a generative adversarial network (GAN) along with a neural architecture search (NAS) in the data preparation and classifier optimization phases. The GAN is innovatively invoked to overcome the class imbalance of the training data by producing the synthetic ECG for PxAF class in a certified manner. The effect of the certified GAN is statistically validated. Instead of using a general-purpose classifier, the NAS automatically designs a highly accurate convolutional neural network architecture customized for the PxAF classification task. Experimental results show that the accuracy of the proposed framework exhibits a high value of 99.0% which not only enhances state-of-the-art by up to 5.1%, but also improves the classification performance of the two widely-accepted baseline methods, ResNet-18, and Auto-Sklearn, by [Formula: see text] and [Formula: see text].

Place, publisher, year, edition, pages
NLM (Medline), 2023
Keywords
Atrial Fibrillation, Electrocardiography, Humans, Machine Learning, Neural Networks, Computer, artificial neural network, human
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-63915 (URN)10.1038/s41598-023-38541-8 (DOI)001030642400009 ()37452165 (PubMedID)2-s2.0-85164756079 (Scopus ID)
Available from: 2023-07-26 Created: 2023-07-26 Last updated: 2023-09-13Bibliographically approved
Mousavi, H., Loni, M., Alibeigi, M. & Daneshtalab, M. (2023). DASS: Differentiable Architecture Search for Sparse Neural Networks. ACM Transactions on Embedded Computing Systems, 22(5 s), Article ID 105.
Open this publication in new window or tab >>DASS: Differentiable Architecture Search for Sparse Neural Networks
2023 (English)In: ACM Transactions on Embedded Computing Systems, ISSN 1539-9087, E-ISSN 1558-3465, Vol. 22, no 5 s, article id 105Article in journal (Refereed) Published
Abstract [en]

The deployment of Deep Neural Networks (DNNs) on edge devices is hindered by the substantial gap between performance requirements and available computational power. While recent research has made significant strides in developing pruning methods to build a sparse network for reducing the computing overhead of DNNs, there remains considerable accuracy loss, especially at high pruning ratios. We find that the architectures designed for dense networks by differentiable architecture search methods are ineffective when pruning mechanisms are applied to them. The main reason is that the current methods do not support sparse architectures in their search space and use a search objective that is made for dense networks and does not focus on sparsity.This paper proposes a new method to search for sparsity-friendly neural architectures. It is done by adding two new sparse operations to the search space and modifying the search objective. We propose two novel parametric SparseConv and SparseLinear operations in order to expand the search space to include sparse operations. In particular, these operations make a flexible search space due to using sparse parametric versions of linear and convolution operations. The proposed search objective lets us train the architecture based on the sparsity of the search space operations. Quantitative analyses demonstrate that architectures found through DASS outperform those used in the state-of-the-art sparse networks on the CIFAR-10 and ImageNet datasets. In terms of performance and hardware effectiveness, DASS increases the accuracy of the sparse version of MobileNet-v2 from 73.44% to 81.35% (+7.91% improvement) with a 3.87× faster inference time.

Place, publisher, year, edition, pages
Association for Computing Machinery, 2023
Keywords
image classification, network sparsification, Neural architecture search, optimization, Deep neural networks, Network architecture, Dense network, Images classification, Neural architectures, Optimisations, Search spaces, Sparse network, Sparse neural networks, Sparsification
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-64424 (URN)10.1145/3609385 (DOI)001074334300008 ()2-s2.0-85171744110 (Scopus ID)
Available from: 2023-10-09 Created: 2023-10-09 Last updated: 2023-10-25Bibliographically approved
Loni, M., Mohan, A., Asadi, M. & Lindauer, M. (2023). Learning Activation Functions for Sparse Neural Networks. In: Proc. Mach. Learn. Res.: . Paper presented at Proceedings of Machine Learning Research. ML Research Press
Open this publication in new window or tab >>Learning Activation Functions for Sparse Neural Networks
2023 (English)In: Proc. Mach. Learn. Res., ML Research Press , 2023Conference paper, Published paper (Refereed)
Abstract [en]

Sparse Neural Networks (SNNs) can potentially demonstrate similar performance to their dense counterparts while saving significant energy and memory at inference. However, the accuracy drop incurred by SNNs, especially at high pruning ratios, can be an issue in critical deployment conditions. While recent works mitigate this issue through sophisticated pruning techniques, we shift our focus to an overlooked factor: hyperparameters and activation functions. Our analyses have shown that the accuracy drop can additionally be attributed to (i) Using ReLU as the default choice for activation functions unanimously, and (ii) Fine-tuning SNNs with the same hyperparameters as dense counterparts. Thus, we focus on learning a novel way to tune activation functions for sparse networks and combining these with a separate hyperparameter optimization (HPO) regime for sparse networks. By conducting experiments on popular DNN models (LeNet-5, VGG-16, ResNet-18, and EfficientNet-B0) trained on MNIST, CIFAR-10, and ImageNet-16 datasets, we show that the novel combination of these two approaches, dubbed Sparse Activation Function Search, short: SAFS, results in up to 15.53%, 8.88%, and 6.33% absolute improvement in the accuracy for LeNet-5, VGG-16, and ResNet-18 over the default training protocols, especially at high pruning ratios.

Place, publisher, year, edition, pages
ML Research Press, 2023
Keywords
Chemical activation, Image enhancement, Machine learning, Activation functions, Condition, Energy, Fine tuning, Hyper-parameter, Hyper-parameter optimizations, Performance, Pruning techniques, Sparse network, Sparse neural networks, Drops
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-66091 (URN)2-s2.0-85184354102 (Scopus ID)
Conference
Proceedings of Machine Learning Research
Available from: 2024-02-26 Created: 2024-02-26 Last updated: 2024-02-26Bibliographically approved
Salimi, M., Loni, M. & Sirjani, M. (2023). Learning Activation Functions for Adversarial Attack Resilience in CNNs. In: Lect. Notes Comput. Sci.: . Paper presented at Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 203-214). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>Learning Activation Functions for Adversarial Attack Resilience in CNNs
2023 (English)In: Lect. Notes Comput. Sci., Springer Science and Business Media Deutschland GmbH , 2023, p. 203-214Conference paper, Published paper (Refereed)
Abstract [en]

Adversarial attacks on convolutional neural networks (CNNs) have been a serious concern in recent years, as they can cause CNNs to produce inaccurate predictions. Through our analysis of training CNNs with adversarial examples, we discovered that this was primarily caused by naïvely selecting ReLU as the default choice for activation functions. In contrast to the focus of recent works on proposing adversarial training methods, we study the feasibility of an innovative alternative: learning novel activation functions to make CNNs more resilient to adversarial attacks. In this paper, we propose a search framework that combines simulated annealing and late acceptance hill-climbing to find activation functions that are more robust against adversarial attacks in CNN architectures. The proposed search method has superior search convergence compared to commonly used baselines. The proposed method improves the resilience to adversarial attacks by achieving up to 17.1%, 22.8%, and 16.6% higher accuracy against BIM, FGSM, and PGD attacks, respectively, over ResNet-18 trained on the CIFAR-10 dataset.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2023
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 14125 LNAI
Keywords
Activation Function, Adversarial Attack, Convolutional Neural Network, Robustness, Activation analysis, Chemical activation, Convolution, Convolutional neural networks, Activation functions, Attack resiliences, High-accuracy, Hill climbing, Neural network architecture, Search method, Training methods, Simulated annealing
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-64441 (URN)10.1007/978-3-031-42505-9_18 (DOI)2-s2.0-85172420687 (Scopus ID)9783031425042 (ISBN)
Conference
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Available from: 2023-10-05 Created: 2023-10-05 Last updated: 2023-10-05Bibliographically approved
Salimi, M., Loni, M., Sirjani, M., Cicchetti, A. & Abbaspour Asadollah, S. (2023). SARAF: Searching for Adversarial Robust Activation Functions. In: ACM International Conference Proceeding Series: . Paper presented at 6th International Conference on Machine Vision and Applications, ICMVA 2023, Singapore, Singapore, 10 March 2023 through 12 March 2023 (pp. 174-182). Association for Computing Machinery
Open this publication in new window or tab >>SARAF: Searching for Adversarial Robust Activation Functions
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2023 (English)In: ACM International Conference Proceeding Series, Association for Computing Machinery , 2023, p. 174-182Conference paper, Published paper (Refereed)
Abstract [en]

Convolutional Neural Networks (CNNs) have received great attention in the computer vision domain. However, CNNs are vulnerable to adversarial attacks, which are manipulations of input data that are imperceptible to humans but can fool the network. Several studies tried to address this issue, which can be divided into two categories: (i) training the network with adversarial examples, and (ii) optimizing the network architecture and/or hyperparameters. Although adversarial training is a sufficient defense mechanism, they suffer from requiring a large volume of training samples to cover a wide perturbation bound. Tweaking network activation functions (AFs) has been shown to provide promising results where CNNs suffer from performance loss. However, optimizing network AFs for compensating the negative impacts of adversarial attacks has not been addressed in the literature. This paper proposes the idea of searching for AFs that are robust against adversarial attacks. To this aim, we leverage the Simulated Annealing (SA) algorithm with a fast convergence time. This proposed method is called SARAF. We demonstrate the consistent effectiveness of SARAF by achieving up to 16.92%, 18.3%, and 15.57% accuracy improvement against BIM, FGSM, and PGD adversarial attacks, respectively, over ResNet-18 with ReLU AFs (baseline) trained on CIFAR-10. Meanwhile, SARAF provides a significant search efficiency compared to random search as the optimization baseline.

Place, publisher, year, edition, pages
Association for Computing Machinery, 2023
Keywords
Activation Function, Adversarial Attack, Convolutional Neural Network, Optimization, Robustness, Chemical activation, Convolution, Convolutional neural networks, Network architecture, Activation functions, Defence mechanisms, Hyper-parameter, Input datas, Large volumes, Network activations, Optimisations, Simulated annealing
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-63891 (URN)10.1145/3589572.3589598 (DOI)2-s2.0-85163400963 (Scopus ID)9781450399531 (ISBN)
Conference
6th International Conference on Machine Vision and Applications, ICMVA 2023, Singapore, Singapore, 10 March 2023 through 12 March 2023
Available from: 2023-07-19 Created: 2023-07-19 Last updated: 2023-07-19Bibliographically approved
Zoljodi, A., Loni, M., Abadijou, S., Alibeigi, M. & Daneshtalab, M. (2022). 3DLaneNAS: Neural Architecture Search for Accurate and Light-Weight 3D Lane Detection. In: Elias Pimenidis; Plamen Angelov; Chrisina Jayne; Antonios Papaleonidas; Mehmet Aydin (Ed.), Artificial Neural Networks and Machine Learning – ICANN 2022: 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part I. Paper presented at ICANN 2022, Bristol, UK, 6-9 September, 2022 (pp. 404-415). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>3DLaneNAS: Neural Architecture Search for Accurate and Light-Weight 3D Lane Detection
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2022 (English)In: Artificial Neural Networks and Machine Learning – ICANN 2022: 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part I / [ed] Elias Pimenidis; Plamen Angelov; Chrisina Jayne; Antonios Papaleonidas; Mehmet Aydin, Springer Science and Business Media Deutschland GmbH , 2022, p. 404-415Conference paper, Published paper (Refereed)
Abstract [en]

Lane detection is one of the most fundamental tasks for autonomous driving. It plays a crucial role in the lateral control and the precise localization of autonomous vehicles. Monocular 3D lane detection methods provide state-of-the-art results for estimating the position of lanes in 3D world coordinates using only the information obtained from the front-view camera. Recent advances in Neural Architecture Search (NAS) facilitate automated optimization of various computer vision tasks. NAS can automatically optimize monocular 3D lane detection methods to enhance the extraction and combination of visual features, consequently reducing computation loads and increasing accuracy. This paper proposes 3DLaneNAS, a multi-objective method that enhances the accuracy of monocular 3D lane detection for both short- and long-distance scenarios while at the same time providing a fair amount of hardware acceleration. 3DLaneNAS utilizes a new multi-objective energy function to optimize the architecture of feature extraction and feature fusion modules simultaneously. Moreover, a transfer learning mechanism is used to improve the convergence of the search process. Experimental results reveal that 3DLaneNAS yields a minimum of 5.2% higher accuracy and ≈ 1.33 × lower latency over competing methods on the synthetic-3D-lanes dataset. Code is at https://github.com/alizoljodi/3DLaneNAS

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2022
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 13529
Keywords
3D lane detection, Autonomous vehicles, Neural architecture search, Cameras, Extraction, Feature extraction, Autonomous driving, Detection methods, Lane detection, Lateral control, Light weight, Multi objective, Neural architectures
National Category
Vehicle Engineering
Identifiers
urn:nbn:se:mdh:diva-60205 (URN)10.1007/978-3-031-15919-0_34 (DOI)000866210600034 ()2-s2.0-85138760578 (Scopus ID)9783031159183 (ISBN)
Conference
ICANN 2022, Bristol, UK, 6-9 September, 2022
Available from: 2022-10-12 Created: 2022-10-12 Last updated: 2024-02-07Bibliographically approved
Loni, M. (2022). Efficient Design of Scalable Deep Neural Networks for Resource-Constrained Edge Devices. (Doctoral dissertation). Västerås: Mälardalens universitet
Open this publication in new window or tab >>Efficient Design of Scalable Deep Neural Networks for Resource-Constrained Edge Devices
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Deep Neural Networks (DNNs) are increasingly being processed on resource-constrained edge nodes (computer nodes used in, e.g., cyber-physical systems or at the edge of computational clouds) due to efficiency, connectivity, and privacy concerns. This thesis investigates and presents new techniques to design and deploy DNNs for resource-constrained edge nodes. We have identified two major bottlenecks that hinder the proliferation of DNNs on edge nodes: (i) the significant computational demand for designing DNNs that consumes a low amount of resources in terms of energy, latency, and memory footprint; and (ii) further conserving resources by quantizing the numerical calculations of a DNN provides remarkable accuracy degradation.

To address (i), we present novel methods for cost-efficient Neural Architecture Search (NAS) to automate the design of DNNs that should meet multifaceted goals such as accuracy and hardware performance. To address (ii), we extend our NAS approach to handle the quantization of numerical calculations by using only the numbers -1, 0, and 1 (so-called ternary DNNs), which achieves higher accuracy. Our experimental evaluation shows that the proposed NAS approach can provide a 5.25x reduction in design time and up to 44.4x reduction in network size compared to state-of-the-art methods. In addition, the proposed quantization approach delivers 2.64% higher accuracy and 2.8x memory saving compared to full-precision counterparts with the same bit-width resolution. These benefits are attained over a wide range of commercial-off-the-shelf edge nodes showing this thesis successfully provides seamless deployment of DNNs on resource-constrained edge nodes.

Place, publisher, year, edition, pages
Västerås: Mälardalens universitet, 2022
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 363
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-59946 (URN)978-91-7485-563-0 (ISBN)
Public defence
2022-10-13, Delta och online, Mälardalens universitet, Västerås, 13:30 (English)
Opponent
Supervisors
Projects
AutoDeep: Automatic Design of Safe, High-Performance and Compact Deep Learning Models for Autonomous VehiclesDPAC - Dependable Platforms for Autonomous systems and Control
Available from: 2022-09-15 Created: 2022-09-14 Last updated: 2022-11-08Bibliographically approved
Loni, M., Zoljodi, A., Majd, A., Ahn, B. H., Daneshtalab, M., Sjödin, M. & Esmaeilzadeh, H. (2022). FastStereoNet: A Fast Neural Architecture Search for Improving the Inference of Disparity Estimation on Resource-Limited Platforms. IEEE Transactions on Systems, Man & Cybernetics. Systems, 52(8), 5222-5234
Open this publication in new window or tab >>FastStereoNet: A Fast Neural Architecture Search for Improving the Inference of Disparity Estimation on Resource-Limited Platforms
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2022 (English)In: IEEE Transactions on Systems, Man & Cybernetics. Systems, ISSN 2168-2216, E-ISSN 2168-2232, Vol. 52, no 8, p. 5222-5234Article in journal (Refereed) Published
Abstract [en]

Convolutional neural networks (CNNs) provide the best accuracy for disparity estimation. However, CNNs are computationally expensive, making them unfavorable for resource-limited devices with real-time constraints. Recent advances in neural architectures search (NAS) promise opportunities in automated optimization for disparity estimation. However, the main challenge of the NAS methods is the significant amount of computing time to explore a vast search space [e.g., 1.6x10(29)] and costly training candidates. To reduce the NAS computational demand, many proxy-based NAS methods have been proposed. Despite their success, most of them are designed for comparatively small-scale learning tasks. In this article, we propose a fast NAS method, called FastStereoNet, to enable resource-aware NAS within an intractably large search space. FastStereoNet automatically searches for hardware-friendly CNN architectures based on late acceptance hill climbing (LAHC), followed by simulated annealing (SA). FastStereoNet also employs a fine-tuning with a transferred weights mechanism to improve the convergence of the search process. The collection of these ideas provides competitive results in terms of search time and strikes a balance between accuracy and efficiency. Compared to the state of the art, FastStereoNet provides 5.25x reduction in search time and 44.4x reduction in model size. These benefits are attained while yielding a comparable accuracy that enables seamless deployment of disparity estimation on resource-limited devices. Finally, FastStereoNet significantly improves the perception quality of disparity estimation deployed on field-programmable gate array and Intel Neural Compute Stick 2 accelerator in a significantly less onerous manner.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
Keywords
Disparity estimation, machine vision, neural architecture search, optimization, transfer learning
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-56844 (URN)10.1109/TSMC.2021.3123136 (DOI)000732342800001 ()2-s2.0-85120087918 (Scopus ID)
Available from: 2021-12-30 Created: 2021-12-30 Last updated: 2022-11-08Bibliographically approved
Loni, M., Mousavi, H., Riazati, M., Daneshtalab, M. & Sjödin, M. (2022). TAS: Ternarized Neural Architecture Search for Resource-Constrained Edge Devices. In: : . Paper presented at Design, Automation and Test in Europe ConferenceDesign, Automation and Test in Europe Conference (DATE) 2022, ANTWERP, BELGIUM (pp. 115-118).
Open this publication in new window or tab >>TAS: Ternarized Neural Architecture Search for Resource-Constrained Edge Devices
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2022 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Ternary Neural Networks (TNNs) compress network weights and activation functions into 2-bit representation resulting in remarkable network compression and energy efficiency. However, there remains a significant gap in accuracy between TNNs and full-precision counterparts. Recent advances in Neural Architectures Search (NAS) promise opportunities in automated optimization for various deep learning tasks. Unfortunately, this area is unexplored for optimizing TNNs. This paper proposes TAS, a framework that drastically reduces the accuracy gap between TNNs and their full-precision counterparts by integrating quantization into the network design. We experienced that directly applying NAS to the ternary domain provides accuracy degradation as the search settings are customized for full-precision networks. To address this problem, we propose (i) a new cell template for ternary networks with maximum gradient propagation; and (ii) a novel learnable quantizer that adaptively relaxes the ternarization mechanism from the distribution of the weights and activation functions. Experimental results reveal that TAS delivers 2.64% higher accuracy and 2.8x memory saving over competing methods with the same bit-width resolution on the CIFAR-10 dataset. These results suggest that TAS is an effective method that paves the way for the efficient design of the next generation of quantized neural networks.

Keywords
Quantization, Ternary Neural Network, Neural Architecture Search, Embedded Systems
National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-56761 (URN)10.23919/date54114.2022.9774615 (DOI)000819484300207 ()2-s2.0-85130852561 (Scopus ID)978-3-9819263-6-1 (ISBN)
Conference
Design, Automation and Test in Europe ConferenceDesign, Automation and Test in Europe Conference (DATE) 2022, ANTWERP, BELGIUM
Projects
DPAC - Dependable Platforms for Autonomous systems and ControlAutoDeep: Automatic Design of Safe, High-Performance and Compact Deep Learning Models for Autonomous Vehicles
Available from: 2021-12-16 Created: 2021-12-16 Last updated: 2024-01-04Bibliographically approved
Vidimlic, N., Levin, A., Loni, M. & Daneshtalab, M. (2021). Image synthesisation and data augmentation for safe object detection in aircraft auto-landing system. In: VISIGRAPP 2021 - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: . Paper presented at 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021, 8 February 2021 through 10 February 2021 (pp. 123-135). SciTePress, 5
Open this publication in new window or tab >>Image synthesisation and data augmentation for safe object detection in aircraft auto-landing system
2021 (English)In: VISIGRAPP 2021 - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, SciTePress , 2021, Vol. 5, p. 123-135Conference paper, Published paper (Refereed)
Abstract [en]

The feasibility of deploying object detection to interpret the environment is questioned in several mission-critical applications leading to raised concerns about the ability of object detectors in providing reliable and safe predictions of the operational environment, regardless of weather and light conditions. The lack of a comprehensive dataset, which causes class imbalance and detection difficulties of hard examples, is one of the main reasons of accuracy loss in attitude safe object detection. Data augmentation, as an implicit regularisation technique, has been shown to significantly improve object detection by increasing both the diversity and the size of the training dataset. Despite the success of data augmentation in various computer vision tasks, applying data augmentation techniques to improve safety has not been sufficiently addressed in the literature. In this paper, we leverage a set of data augmentation techniques to improve the safety of object detection. The aircraft in-flight image data is used to evaluate the feasibility of our proposed solution in real-world safety-required scenarios. To achieve our goal, we first generate a training dataset by synthesising the images collected from in-flight recordings. Next, we augment the generated dataset to cover real weather and lighting changes. Introduction of artificially produced distortions is also known as corruptions and has since recently been an approach to enrich the dataset. The introduction of corruptions, as augmentations of weather and luminance in combination with the introduction of artificial artefacts, is done as an approach to achieve a comprehensive representation of an aircraft’s operational environment. Finally, we evaluate the impact of data augmentation on the studied dataset. Faster R-CNN with ResNet-50-FPN was used as an object detector for the experiments. An AP@[IoU=.5:.95] score of 50.327% was achieved with the initial setup, while exposure to altered weather and lighting conditions yielded an 18.1% decrease. The introduction of the conditions into the training set led to a 15.6% increase in comparison to the score achieved from exposure to the conditions. 

Place, publisher, year, edition, pages
SciTePress, 2021
Keywords
Data augmentation, Object detection, Safety, Situational awareness, Synthesised image, Aircraft detection, Aircraft landing, Computer graphics, Computer vision, Lighting, Object recognition, Training aircraft, Aircraft auto landing, Light conditions, Lighting conditions, Mission critical applications, Object detectors, Operational environments, Training dataset
National Category
Computer Vision and Robotics (Autonomous Systems) Vehicle Engineering
Identifiers
urn:nbn:se:mdh:diva-53797 (URN)10.5220/0010248801230135 (DOI)000661288200011 ()2-s2.0-85102976147 (Scopus ID)9789897584886 (ISBN)
Conference
16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021, 8 February 2021 through 10 February 2021
Available from: 2021-04-01 Created: 2021-04-01 Last updated: 2022-11-25Bibliographically approved
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