An Enhanced Deep Learning Model for Obstacle and Traffic Light Detection Based on YOLOv5

Abstract

Timely detection of dynamic and static obstacles and accurate identification of signal lights using image processing techniques is one of the key technologies for guidance robots and is a necessity to assist blind people with safe travel. Due to the complexity of real-time road conditions, current obstacle and traffic light detection methods generally suffer from missed detection and false detection. In this paper, an improved deep learning model based on YOLOv5 is proposed to address the above problems and to achieve more accurate and faster recognition of different obstacles and traffic lights that the blind may encounter. In this model, a coordinate attention layer is added to the backbone network of YOLOv5 to improve its ability to extract effective features. Then, the feature pyramid network in YOLOv5 is replaced with a weighted bidirectional feature pyramid structure to fuse the extracted feature maps of different sizes and obtain more feature information. Finally, a SIoU loss function is introduced to increase the angle calculation of the frames. The proposed model’s detection performance for pedestrians, vehicles, and traffic lights under different conditions is tested and evaluated using the BDD100K dataset. The results show that the improved model can achieve higher mean average precision and better detection ability, especially for small targets.