A fusion adaptive recognition network based on intensity and polarization imaging

As a new photoelectric detection method, polarization imaging can effectively improve the detection range and recognition accuracy of key targets under harsh environmental conditions by its excellent imaging effect of de-fog reconstruction. This paper proposes an image detection and recognition network based on polarization information and intensity information. The network is based on the yolov5 network integrated with DYHEAD block for recognition, realizing scale perception, space perception and task perception in a unified manner. Meanwhile, Res2Net is integrated for multi-scale characterization at the granularity level. Finally, the attention mechanism is introduced to realize the adaptive extraction and fusion of multi-scale features at the granularity level. The results show that the proposed method can effectively improve the recognition accuracy in low visibility environment.