改进YOLO检测小交通标志，提升效率与精度。
YOLO-PPA based Efficient Traffic Sign Detection for Cruise Control in Autonomous Driving
- 在YOLO的C2F模块中引入部分卷积，提升效率
- 在GTSDB上推理速度提升11.2%，mAP50达93.2%
- 适合嵌入式车载系统部署，兼顾速度与准确率
在自动驾驶系统中，高效准确地检测交通标志至关重要。然而，距离越远，交通标志越小，现有目标检测算法难以识别这些小尺度标志。此外，车载嵌入式设备的性能限制了检测模型的规模。为此，本文提出一种基于YOLO-PPA的交通标志检测方法。在GTSDB数据集上的实验结果表明，相比原始YOLO，该方法推理效率提升11.2%，mAP50提升至93.2%，验证了所提方法的有效性。
原文摘要 · Abstract (English)
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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