Abstract
Due to the problem of reduced visibility caused by haze pollution, it has brought great inconvenience to outdoor activities and traffic travel. Therefore, haze control has become one of the topics closely concerned by all sectors of society. Real time and accurate visibility detection is one of the important links to effectively prevent the impact of sudden fog or haze on driving safety, but the cost performance, accuracy and popularity of existing methods and equipment need to be improved. In this paper, we proposed a vision-based haze visibility detection method that achieves a better balance in the abovementioned aspects. Specifically, an improved AlexNet network with multi-scale feature mechanism is used to capture richer haze spatial details. Then, channel attention is employed to emphasize the characters of the feature maps. Finally, densely connection network is utilized to improve the contribution of the interconnected information flows. The experimental results show that the algorithm is consistent with the human observation, which meets the safety requirements. Moreover, our method achieves 95.6% accuracy with 11.68% improvement.
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Tao, J., Wu, Y., Shao, Q., Yan, S. (2022). Multi-scale Feature Based Densely Channel Attention Network for Vision-Based Haze Visibility Detection. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13458. Springer, Cham. https://doi.org/10.1007/978-3-031-13841-6_51
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DOI: https://doi.org/10.1007/978-3-031-13841-6_51
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