Object Detection With Physical Prior and AWConv in Foggy Weather for Traffic Scenes.

Abstract

Despite significant advances in object detection methods for traffic scenes, object detection under adverse weather conditions is still a challenging task. Especially in foggy weather, the presence of fog reduces visibility, thus weakening the feature information of traffic objects in images, and foggy weather occurs frequently. To cope with this problem, we propose an object detection method with physical prior and adaptive weight convolution (AWConv), and evaluate it on datasets such as Foggy Cityscapes and RTTS. We apply gamma correction in the improved defogging algorithm to enhance the key regions in the image, thus improving the separability of the features. Meanwhile, the feature extraction and representation ability of the model is enhanced by an adaptive weighting mechanism, which in turn improves the model detection performance. In addition, we explore the relationship between image quality and detection accuracy and observe that they are not linearly positively correlated. Due to the complexity of traffic objects in foggy weather, we conduct experiments on Foggy Cityscapes (synthetic fog), RTTS (real-world multiple adverse weather), Cityscapes (normal weather), and extended dataset (different fog concentrations) to validate the model's effectiveness, generalization ability, and robustness. Experimental results show that the small model alone improves mean average precision (mAP) by 1.4% with only 24.6 giga floating point operations per second (GFLOPs) on the Foggy Cityscapes dataset, reduces GFLOPs by 3.8 and improves recall (R) by 1.1% on the RTTS dataset.