Traffic Sign Detection Based on Driving Sight Distance in Haze Environment

To explore the relationship between traffic sign detection performance and driving sight distance in haze environment, this paper proposed a UV correlation model among sight distance, haze grade and traffic sign detection performance. First, the German traffic sign data set (GTSDB) is synthesized into experimental data set according to three levels of light haze, haze and dense haze. The Faster R-CNN model is utilized to detect the traffic signs after dehazing by Guided Filter Dehazing Algorithm. The detection accuracy is as high as 95.11%, which shows that the model has strong generalization ability and adaptability. Second, the weight is determined by haze, taking the driving sight distance as U layer and the detection result of Faster RCNN model as V layer, establishing the UV correlation model. Finally, KM algorithm is used to solve the correlation model, and the best matching result between UV layers is gained. The experimental results show the haze level significantly affects the driving sight distance, and then affects the detection accuracy of traffic signs. When the driving Sight distance threshold is 300 meters, 100 meters and 50 meters in light haze, haze and dense haze, the KM algorithm obtains the detection accuracy levels of A (higher than 93%), B (88%-93%) and C (85%-88%), respectively.