A remote sensing image target detection method based on multi-scale feature enhancement

Compared to traditional image target detection, the scale variation of the targets to be detected in remote sensing images presents significant differences. The presence of other objects in complex backgrounds greatly interferes with the accuracy of target detection. This would make it difficult for the model to capture crucial detailed information about these targets, thereby impacting detection accuracy. Therefore, this paper proposes an improved algorithm aimed at enhancing the model's perception of multi-scale targets and improving detection accuracy. Firstly, we propose the SPPCSPC-NCA module, which accelerates the model inference speed while maintaining the same receptive field through the maximum pooling layer and Hardswish activation function in a series operation. Secondly, to reduce feature loss, this paper uses a sub-pixel convolution upsampling module to better capture detail features. Finally, the ASFF module is used to achieve adaptive weighted fusion of different scale features, further improving detection performance. Experimental results show that on the DIOR dataset, the improved model has increased by 1.5% and 2.1% on mAP50 and mAP50:95 respectively, verifying the effectiveness of the algorithm improvement proposed in this paper.