Detection of pedestrians and vehicles on foggy roads based on improved YOLOv7

Abstract Nowadays, object detection technology plays an indispensable role in the direction of automatic vehicle driving. However, inclement weather, such as fog and haze, poses severe challenges to the ability to understand visual perception and scene understanding of automatic driving technology, making it extremely difficult. Therefore, this paper proposes an improved YOLOv7 algorithm. Firstly, Gaussian blur and Gaussian noise are used to process the original data set, reducing the sensitivity of our model to image information, improving the ability to adapt to changing environments and tasks, and enhancing the generalization performance. Then, using the SIOU loss function, we optimize the algorithm to accelerate the convergence speed while considering the vector angle problem. Add Coordinate Attention integrates accurate position information into the coding network, optimizes the recognition accuracy of the model for a specific position, significantly improves its overall feature extraction ability, and captures key features more accurately, thereby improving the overall performance, capturing key information, and reducing the possibility of missed detection. The introduction of CoordConv into ELAN in the backbone allows for a more sensitive perception of the position of objects during image processing, thus improving the accuracy of its recognition and positioning. The RepGhostNet structure is introduced into the neck to realize the implicit reuse of features. According to the experiments in this paper, our improved YOLOv7 algorithm achieves 87.3% map in the RTTS data set and partial synthetic fog KITTI data set, which improves by 3.9% based on the original model.