Abstract
With the development of automatic driving technology, traffic sign detection has become a very important task. However, it is a challenging task because of the complex traffic sign scene and the small size of the target. In recent years, a number of convolutional neural network (CNN) based object detection methods have brought great progress to traffic sign detection. Considering the still high false detection rate, as well as the high time overhead and computational overhead, the effect is not satisfactory. Therefore, we employ lightweight network model YOLO v5 (You Only Look Once) as our work foundation. In this paper, we propose an improved YOLO v5 method by using balances feature pyramid structure and global context block to enhance the ability of feature fusion and feature extraction. To verify our proposed method, we have conducted a lot of comparative experiments on the challenging dataset Tsinghua-Tencent-100K (TT100K). The experimental results demonstrate that the mAP@.5 and mAP@.5:0.95 are improved by 1.9% and 2.1%, respectively.
Highlights
Since the rapid development of automatic driving technology, great changes have taken place in people’s daily life
To get higher prediction speed with lightweight network model, YOLO v5 is employed as the baseline method, which is one of the best object detection methods with excellent detection accuracy and very low time complexity, especially suitable for intelligent driving
The experimental results show that the accuracy and recall of our model are obviously improved over the baseline method YOLO v5, mAP@.5 and mAP@.5:0.95 are improved by 1.9% and 2.1%, respectively
Summary
Since the rapid development of automatic driving technology, great changes have taken place in people’s daily life. With the evolution of deep learning, many people attempt to use convolutional neural network (CNN) based object detection methods to detect traffic sign, such as YOLO [1] and SSD [2] (Single Shot MultiBox Detector). To get higher prediction speed with lightweight network model, YOLO v5 is employed as the baseline method, which is one of the best object detection methods with excellent detection accuracy and very low time complexity, especially suitable for intelligent driving.
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