N_ResNet: A Real-Time Traffic Light Recognition Network Using Object Detection

Abstract

Traffic light recognition is a critical requirement in autonomous driving. Incorrect recognition of traffic lights may lead to serious traffic accidents. Traditional traffic light recognition methods are categorized into model-based methods, semantic-segmentation-based methods, and object-detection-based methods. Model-based methods are strongly influenced by the lighting and environment. Although semantic-segmentation-based methods can detect traffic lights well, their time cost is high. Object-detection-based methods can satisfy the requirements of real-time traffic light recognition in terms of performance and time. This study proposes a real-time traffic light recognition algorithm that first extracts traffic lights from images using the YOLOv5s object detection algorithm based on the N_ResNet network and then classifies the state of the extracted traffic light images using the SVM classifier. To address the inconsistent size of traffic lights in real scenes and the large number of medium-size traffic lights, this study proposes a new network structure called N_ResNet. N_ResNet can effectively detect traffic lights of different sizes, especially those of medium size. It is similar to the three-layer ResNet network structure; the main difference is that the size of the convolutional kernel of the first convolutional layer is 3. We apply N_ResNet to the YOLOv5s object detection algorithm for traffic light detection. The results show that the proposed network achieves a significant improvement in the recall compared to DarkNet. The overall recall is improved by 2.66%, including 0.47% for traffic lights in the daytime and 11.32% for traffic lights in the nighttime. In addition, the accuracy of the SVM classifier reaches 92.2%.

A Deep Learning Method for Traffic Light Status Recognition

“…Currently, status-recognition methods for traffic lights include machine-learning and deep-learning-based methods (Zeng et al, 2023). The former method achieves traffic light status recognition by manual feature segmentation of region of interest (ROI) and combining it with classifiers.…”