Traffic Light Detection Based on Deep Learning

This article proposes a traffic signal recognition algorithm based on deep learning, addressing prominent issues in traffic signal light detection, such as low detection accuracy, slow speed, and large volume making it difficult to install on mobile devices. The algorithm is built upon the YOLOv5S network module and employs Convolutional Neural Network (CNN) for feature extraction and classification. Two image enhancement techniques, adaptive anchor boxes and adaptive image scaling, are used to improve detection speed and accuracy. Additionally, the Mosaic data augmentation technique is applied to enhance the accuracy and robustness of traffic signal light recognition. Extensive comparative experiments were conducted using a self-collected traffic signal light database. The results indicate that the proposed method, compared to traditional approaches, effectively handles interference factors such as lighting conditions, background variations, and changes in viewing angles. It achieves high accuracy and practicality in traffic signal light recognition, with an average precision rate of 93%. This method enables rapid and accurate detection of traffic signal lights and can be widely applied in traffic signal analysis research.