A novel UAV target detection method based on the improved YOLOv4-tiny network

Xia Liu; Ruihong Han; Xiaojun Mei; Yanglin Hu

doi:10.1117/12.2684209

1 August 2023 A novel UAV target detection method based on the improved YOLOv4-tiny network

Xia Liu, Ruihong Han, Xiaojun Mei, Yanglin Hu

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127541T (2023) https://doi.org/10.1117/12.2684209
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

Aiming at the poor detection performance of UAV aerial photography based on deep learning on small targets and occlusion targets, the paper proposed an improved UAV aerial photography detection model based on YOLOv4-Tiny. By employing a lightweight YOLOv4-Tiny framework, a multi-scale detection strategy is first introduced to expand the detection scope. Moreover, the hybrid deep and shallow semantic information are utilized to enrich the feature of small and obscured targets. Second, the Efficient channel attention module is added to the enhanced feature extraction network to redistribute channel weights and the secondary screening of feature information, so as to improve target detection ability. To evaluate the proposed model, the paper used a PASCAL VOC data set and the corresponding visualization detection was carried out on the aerial photography data set. The results showed that the proposed model's average accuracy (mAP) is improved by 4.19%, and more importantly, the missing detection rate of both small targets and occlusion targets is effectively reduced. Finally, the proposed model was deployed on the Nvidia Jetson TX2 platform, and the aerial photography experiment showed that the FPS of the model on TX2 reached 30.2, meeting the real-time requirements of UAV aerial photography detection.

Citation Download Citation

Xia Liu, Ruihong Han, Xiaojun Mei, and Yanglin Hu "A novel UAV target detection method based on the improved YOLOv4-tiny network", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127541T (1 August 2023); https://doi.org/10.1117/12.2684209

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