基于深度学习的交通信号灯快速检测与识别

doi:10.11896/jsjkx.190400026

Abstract

Abstract: Traffic light detection and recognition technology can help drivers make correct driving decisions,reduce traffic accidents,and provide security for unmanned driving.Aiming at the technical difficulties such as the complex and variable traffic light detection scene,and targets typically account for a very small percentage of the dataset images,a fast detection and recognition algorithm for traffic light based on deep learning was proposed.The overall framework consists of three parts:heuristic-based image pre-segmentation,which is used to narrow the search range and improve the relative size and detection accuracy of the traffic light panel in the input images;detection and recognition based on deep learning,using convolutional neural networks to detect and identify traffic lights accurately;NMS (Non-Maximum Suppression) algorithm,which is used to remove the repeated detections of the previous stage.The proposed Split-CS-Yolo model achieves 96.08% mAP and 2.87% miss detection rate on the LISA dataset.Compared with other methods of the Yolo series,it not only has higher accuracy and lower missed detection rate,but also reduces the model size to 8.6% of the original Yolov2,thus increasing the detection speed by 63%.

Key words: Deep learning, Fast detection, Image pre-segmentation, NMS, Traffic light detectionand recognition

CLC Number:

TP391

QIAN Hong-yi, WANG Li-hua, MOU Hong-lei. Fast Detection and Identification of Traffic Lights Based on Deep Learning[J].Computer Science, 2019, 46(12): 272-278.

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