Dehazing and Detection of Foggy Traffic Images Using Bilateral Filtering and Improved YOLOX-S Algorithm

To address the issues of speed and accuracy in traffic image target detection under foggy conditions, this paper proposes a dehazing algorithm using bilateral filtering and an improved YOLOX-S algorithm for foggy traffic image target detection. The bilateral filter is integrated into the Retinex algorithm to perform image dehazing and enhance the quality of foggy images. Based on the improved YOLOX-S architecture, deep separable convolutions are introduced to replace traditional convolutions in the FPN and YOLOHead residual structures, thereby reducing the model's parameter count. Additionally, the ECANet channel attention mechanism is incorporated to enhance the network's focus, reducing false positive and false negative rates in traffic image target detection under foggy conditions. Experimental results show that the bilateral filtering algorithm effectively improves the quality of foggy images, achieving a peak signal-to-noise ratio (PSNR) of 24.032 dB and a structural similarity index (SSIM) of 0.873. The improved YOLOX-S algorithm demonstrates precise target detection, with an average precision of 98% and a detection frame rate of 23 frames per second.