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.
The geometric and motion characteristics of false targets are more similar to those of real targets. The detection accuracy of traditional infrared radiation characteristics methods for infrared targets is obviously low. For small infrared weak targets with small size and low signal-to-noise ratio, it is difficult to effectively extract shape features by using a single band detection method. Therefore, this paper makes full use of the advantages of infrared single band image and multispectral image target detection methods, and combines single band detection technology and multispectral detection technology, which not only avoids the defect of single band detection missing alarm, but also reduces the impact of large amount of multispectral detection data, and has real-time performance and good detection effect. The hybrid detection algorithm proposed in this paper has stronger adaptability, higher recognition rate and faster processing ability.
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