Target detection of UAV aerial photography based on improved  YOLOv5

Chongyang Du; Yanwen Zhang

doi:10.1117/12.2681347

8 June 2023 Target detection of UAV aerial photography based on improved YOLOv5

Chongyang Du, Yanwen Zhang

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 1270734 (2023) https://doi.org/10.1117/12.2681347
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

In view of the traditional aerial image of UAV, the target detection method has many shortcomings, such as inaccurate detection and positioning, low detection accuracy and so on. Based on the original YOLOv5 framework, this paper proposes a new target recognition algorithm and an improved feature extraction method, and uses ultra-lightweight convolution neural network MobileV3 to replace the main feature extraction network of YOLOv5 network; In order to improve the attention of the model to other regions, the attention mechanism GAM Attention module is introduced. This mechanism embeds the location information into the channel attention, realizes multi-scale processing and feature fusion, and makes the model have higher detection accuracy. The experiment shows that the size of the improved lightweight network model is only 81.7% of the original YOLOv5 model, and the mAP on the data set reaches 96.8%. This method greatly reduces the model parameters and the amount of computation on the basis of ensuring the detection accuracy, and also greatly improves the detection speed and accuracy. Therefore, the method proposed in this paper can provide strong support for the application of UAV target detection.

Citation Download Citation

Chongyang Du and Yanwen Zhang "Target detection of UAV aerial photography based on improved YOLOv5", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 1270734 (8 June 2023); https://doi.org/10.1117/12.2681347

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