YOLO-PPA based Efficient Traffic Sign Detection for Cruise Control in Autonomous Driving
This addresses efficient traffic sign detection for autonomous driving systems, but it is incremental as it builds on existing YOLO methods.
The paper tackled the problem of detecting small traffic signs at long distances in autonomous driving by proposing a YOLO-PPA based algorithm, which improved inference efficiency by 11.2% and mAP 50 by 93.2% on the GTSDB dataset.
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.