Improved object detection in YOLOv7 aerial views

Zijian Li; Chaobing Huang

doi:10.1117/12.3031172

19 July 2024 Improved object detection in YOLOv7 aerial views

Zijian Li, Chaobing Huang

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 1318120 (2024) https://doi.org/10.1117/12.3031172
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

With the development of unmanned aerial vehicle (UAV) technology, object detection technology has been widely used in UAVs, realizing object detection and recognition of ground scenes from the perspective of aerial photography. To further enhance the performance of object detection from an aerial photography perspective, an improved YOLOv7 network is introduced in this paper, termed as YOLOv7-Vis. Firstly, data augmentation methods are used to improve the imbalance of samples in the UAV data set. Secondly, an additional prediction head is added to detect small-scale targets. Thirdly, the SimAM attention module is inserted into YOLOv7 by discussing the performance of different attention combinations. Finally, the experimental results show that the method proposed in this paper improves the mAP@0.5 by 5.9% and the mAP@0.5:0.95 by 3.6% over the original YOLOv7 on the VisDrone dataset.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zijian Li and Chaobing Huang "Improved object detection in YOLOv7 aerial views", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 1318120 (19 July 2024); https://doi.org/10.1117/12.3031172

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