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.
In current image fusion techniques, image fusion is usually performed on a dual-band image to obtain a fused image with significant target information, or on an intensity image and a polarization image to obtain an image with stronger visual perception. If more information is to be obtained in a single image, tri-band fusion and intensity/polarization image fusion techniques can be combined. In order to solve the above problems, in this paper, we have acquired some tri-band polarization images through a common aperture multispectral polarization photoelectric device, which contains intensity and polarization visible (VIS) images, as well as the intensity images of near-infrared (NIR) and long-wave infrared (LIR). Besides, in order to obtain good image fusion results, we built an end-to-end self-supervised image fusion network and designed an efficient loss function to train the network. We conducted experiments on TPFNet on the acquired dataset and compared it with other image fusion algorithms. The results show that TPFNet achieves excellent results in both subjective and objective evaluations.
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