PRO-YOLOv4-tiny: towards more balance between accuracy and speed in the detection of small targets in remotely sensed images.

Aiming at the problem of low accuracy of small target detection in Unmanned Aerial Vehicle (UAV) aerial remote sensing images and limited computing resources of UAV platform, this paper proposes a novel real-time detection algorithm for aerial remote sensing images. Firstly, the Spatial Pyramid Pooling-Fast (SPPF) is used to fuse the global and local features in different receptive fields. Second, we propose the Drone-captured Path Aggregation Network (CPAN) to enrich the semantic features of small targets while keeping the model lightweight. CPAN adds a new detection layer and uses the fusion of deep and shallow feature information to enhance the detection of small targets. At the same time, it uses depthwise separable convolution (DSC) to reduce the number of parameters. Then, Coordinate Attention (CA) is used to capture the cross-channel information with direction-aware and position-aware information. Finally, Decoupled-Head is introduced to make the detection of classification and coordinate regression more robust. We evaluate our model based on the aerial remote sensing dataset. The experimental results show that the proposed method provides a better balance between accuracy and speed than other lightweight networks. [ABSTRACT FROM AUTHOR]