The algorithm for traffic sign recognition in hazy weather is enhanced by incorporating an improved generative adversarial network
The recognition of traffic signs,crucial for ensuring standardized vehicle driving,is significantly impaired by smogs.To enhance the accuracy of traffic sign recognition in smoggy weather,this paper proposes an algorithm based on an improved Generative Adversarial Network (GAN)specifically designed for smoggy scenarios.The algorithm consists of two main parts:first,a multi-scale GAN is employed to defog images by incorporating multi-scale convolution and perceptual loss functions into the generator. Multi-scale convolution facilitates feature extraction while perceptual loss preserves high-level semantic information like content and style in depth features,resulting in defogging effects that align better with human visual perception.Second,for traffic sign recognition,a smaller 160 ×160 detection layer with a reduced receptive field is added to the original YOLOX-S model to minimize missed detection of small-scale traffic signs.Meanwhile,a coordinate attention (CA)mechanism is introduced into the backbone network to strengthen feature representation.Our experimental results demonstrate the proposed defogging model outperforms other representative algorithms in such evaluation indices as PSNR and SSIM.Moreover,compared to the original model,our traffic sign recognition algorithm improves accuracy by 2％,mAP value by 4％,and recall rate by 7％,demonstrating its fair effectiveness.
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