@inproceedings{0f9fd4f1290c48bf98143b1f69f10ac4,
title = "A fusion adaptive recognition network based on intensity and polarization imaging",
abstract = "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.",
keywords = "Convolutional neural network, Multidimensional information fusion, Polarization imaging, Target recognition",
author = "Ning Ma and Yunan Wu and Jun Chang and Wancheng Liu and Yining Yang and Jinjin Wang and Xin Liu",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE. All rights reserved.; 6th Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition ; Conference date: 22-10-2023 Through 24-10-2023",
year = "2024",
doi = "10.1117/12.3025945",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Chao Zuo and Jiangtao Xu",
booktitle = "Sixth Conference on Frontiers in Optical Imaging and Technology",
address = "United States",
}