Nighttime Visibility Classification Based on Stable Light Sources
Zhuoran Liang, Yu Cao, Zhilei Wang, Yongqiang Li, Zan Chen, Ting Sun · IEEE Access · 2024
To enhance the accuracy of existing nighttime visibility estimation methods, this paper proposes a classification algorithm for nighttime visibility levels based on stable light sources. Initially, a target detection network identifies all stable streetlights in the image and extracts light source blocks. Subsequently, these blocks undergo fog classification through a classification network. The blocks are then sorted by brightness values and assigned corresponding weights. Finally, the classification results and weights are combined to categorize the nighttime image visibility levels. Experimental results show that the accuracy of our nighttime visibility classification algorithm reaches 77.6% on real-world datasets, outperforming existing methods and demonstrating good generalization across different scenes.