Research on Target Recognition Based on Deep Feature Fusion of Infrared and Visible Light Images

It is expected that unmanned aerial vehicle remote sensing target recognition will have a broad range of applications in fields such as smart cities, traffic monitoring, and disaster monitoring. However, remote sensing images have relatively small targets, and they are also affected by factors such as weather, lighting, and occlusion. This makes target detection and recognition difficult. In order to improve the accuracy of target recognition, this paper proposes a dual modal image depth feature fusion method for recognizing unmanned aerial vehicle remote sensor images. The method includes dual modal image registration, and a dual modal image depth feature fusion method based on the yolov7 model. By constructing a bimodal dataset and using the HOPC operator for image registration, strict registration was achieved with manual annotation assistance. The yolov7-dual model proposed in this article, through pixel level fusion and CAFM attention mechanism, ultimately increased the mAP value from 0.424 to 0.657, significantly exceeding expectations.