YOLO-PPA based Efficient Traffic Sign Detection for Cruise Control in Autonomous Driving

2024/09/05 by Jingyu Zhang, Zhang, Jingyu, Wenqing Zhang +7 · 8 citations
Computer Science · Engineering · Neuroscience · #Advanced Neural Network Applications #Artificial Intelligence (cs.AI) #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Vehicle License Plate Recognition

paper · pdf · doi:10.48550/arxiv.2409.03320

openalex publication_date 2024/09/05 · openalex created_date 2024/10/19 · openalex updated_date 2026/07/28

Abstract

It is very important to detect traffic signs efficiently and accurately in autonomous driving systems. However, the farther the distance, the smaller the traffic signs. Existing object detection algorithms can hardly detect these small scaled signs.In addition, the performance of embedded devices on vehicles limits the scale of detection models.To address these challenges, a YOLO PPA based traffic sign detection algorithm is proposed in this paper.The experimental results on the GTSDB dataset show that compared to the original YOLO, the proposed method improves inference efficiency by 11.2%. The mAP 50 is also improved by 93.2%, which demonstrates the effectiveness of the proposed YOLO PPA.

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