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Improved Object Detection Method Utilizing YOLOv7-Tiny for Unmanned Aerial Vehicle Photographic Imagery
Department of Computer Engineering, Taiyuan Institute of Technology, Taiyuan, 030008, China.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-9857-4317
School of Computer Science and Technology, Taiyuan Normal University, Jinzhong, 030619, China.
Artificial Intelligence Institute of Shanghai University, Shanghai University, Shanghai, 200444, China .
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2023 (English)In: Algorithms, E-ISSN 1999-4893, Vol. 16, no 11, article id 520Article in journal (Refereed) Published
Abstract [en]

In unmanned aerial vehicle photographs, object detection algorithms encounter challenges in enhancing both speed and accuracy for objects of different sizes, primarily due to complex backgrounds and small objects. This study introduces the PDWT-YOLO algorithm, based on the YOLOv7-tiny model, to improve the effectiveness of object detection across all sizes. The proposed method enhances the detection of small objects by incorporating a dedicated small-object detection layer, while reducing the conflict between classification and regression tasks through the replacement of the YOLOv7-tiny model’s detection head (IDetect) with a decoupled head. Moreover, network convergence is accelerated, and regression accuracy is improved by replacing the Complete Intersection over Union (CIoU) loss function with a Wise Intersection over Union (WIoU) focusing mechanism in the loss function. To assess the proposed model’s effectiveness, it was trained and tested on the VisDrone-2019 dataset comprising images captured by various drones across diverse scenarios, weather conditions, and lighting conditions. The experiments show that mAP@0.5:0.95 and mAP@0.5 increased by 5% and 6.7%, respectively, with acceptable running speed compared with the original YOLOv7-tiny model. Furthermore, this method shows improvement over other datasets, confirming that PDWT-YOLO is effective for multiscale object detection. 

Place, publisher, year, edition, pages
Multidisciplinary Digital Publishing Institute (MDPI) , 2023. Vol. 16, no 11, article id 520
Keywords [en]
decoupled head, small-object detection, WIoU, YOLOv7-tiny model, Aerial photography, Antennas, Image enhancement, Object recognition, Unmanned aerial vehicles (UAV), Aerial vehicle, Loss functions, Object detection method, Objects detection, Photographic imagery, Small object detection, Small objects, Wise intersection over union, Object detection
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:mdh:diva-65015DOI: 10.3390/a16110520ISI: 001115260900001Scopus ID: 2-s2.0-85178308460OAI: oai:DiVA.org:mdh-65015DiVA, id: diva2:1819306
Available from: 2023-12-13 Created: 2023-12-13 Last updated: 2023-12-20Bibliographically approved

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Xiong, Ning

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