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.
At present, the two-dimensional images obtained by existing small lenses cannot effectively reflect the three-dimensional structure, and many surface defects in4 industry have obvious three-dimensional morphological changes[1]. Therefore, three-dimensional small optical systems are the development direction of industrial optical technology[2]. In view of the characteristics of small lens, small aperture, and single imaging channel, this paper uses a monocular stereoscopic vision method to iteratively reconstruct depth information, and designs a stray light elimination optical system that can obtain both traditional images and three-dimensional stereoscopic images. , improved the photometric stereo vision algorithm used in traditional telecentric systems, developed close-range 3D imaging suitable for small lens systems and improved the accuracy of 3D imaging.
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