Object Detection of UAV Images from Orthographic Perspective Based on Improved YOLOv5s

Feng Lu, Kewei Li (), Yunfeng Nie, Yejia Tao, Yihao Yu, Linbo Huang and Xing Wang
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Feng Lu: College of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China
Kewei Li: Institute of Digital Economy, Nanchang Hangkong University, Nanchang 330063, China
Yunfeng Nie: College of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China
Yejia Tao: College of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China
Yihao Yu: College of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China
Linbo Huang: College of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China
Xing Wang: School of Atmosphere Science, Nanjing University, Nanjing 210023, China

Sustainability, 2023, vol. 15, issue 19, 1-20

Abstract: Object detection methods of UAV (Unmanned Aerial Vehicle) images are greatly improved with the development of UAV technology. In comparison, the existing object detection methods of UAV images lack outstanding performance in the face of challenges such as small targets, dense scenes, sparse distribution, occlusion, and complex background, especially prominent in the task of vehicle detection. This paper proposed an improved YOLOv5s method to perform vehicle detection of UAV images. The CA (Coordinate Attention) is first applied to the neck of YOLOv5s to generate direction-aware and position-sensitive feature maps, respectively, to improve the detection accuracy of sparsely distributed vehicle targets in complex backgrounds. Then, an improved PAFPN (Path Aggregation Feature Pyramid Network) at the neck of YOLOv5s is proposed for more efficient detection of small and dense vehicle targets. Finally, the CIoU (Complete Intersection Over Union) loss function was used to calculate the bounding box regression to obtain a more comprehensive overlap measure to accommodate different shapes of vehicle targets. We conducted extensive experiments on the self-built UAV-OP (Unmanned Aerial Vehicle from Orthographic Perspective) dataset. The experimental results show that our method achieves the best detection accuracy with a small quantity of calculation increase compared with YOLOv5s. The mAP50 improved by 3%, and the mAP50:95 improved by 1.7% with a 0.3 GFlops increase.

Keywords: UAV images; YOLOv5s; deep learning; coordinate attention; improved PAFPN; CIoU loss (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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