Research on small target detection algorithm for UAV inspection scene based on SPD-conv

For the UAV inspection process to detect the target size is small, the distribution of dense and serious obstruction and other reasons leading to the target detection difficult problem, a small target detection algorithm for UAV inspection scene based on SPD-Conv is proposed. SPD-Conv is a convolutional layer of a deep learning model, which contains a space-to-depth layer and a non-step-size convolutional layer, and SPD-Conv is a deep learning model. Conv replaces each step-spanning convolutional and pooling layer to reduce the loss of detail characteristics during downsampling and improve network's performance in recognising small targets. By adding a 160×160 small-target detection head and deleting large-target detection head, SA attention mechanism is added before each detection head to reduce interference and improve network's ability to extract features from small targets. Experimental results on VisDrone2019 dataset show that the improved model has improved misdetection and false detection phenomena. The detection accuracy of improved model is 44%, which is 6.6 percent higher than that of YOLOv5s model, and speed reaches 84.03 FPS. The improved model can fulfil real-time detection requirements and detection accuracy is improved.