Deep Parallel Structure Network for Multi-Scale Target Detection in Remote Sensing Images

This paper constructs a detection network that combines convolutional neural network and Transformer in parallel to address the challenges of multi-scale target detection in remote sensing images. The network utilizes global and local information interaction to further improve the effectiveness of multi-scale object detection tasks. Additionally, the network introduces both top-down and bottom-up pathways to fuse multi-scale information, and employs coordinate attention mechanism to perform feature selection. The proposed network is compared with some existing networks on the LEVIR remote sensing image dataset, and the results show that the proposed network achieves higher average detection accuracy in terms of multi-scale target detection, particularly for small objects.