Aiming at solving the problem of missed detection and low accuracy in detecting traffic signs
in the wild, an improved method of YOLOv8 is proposed. Firstly, combined with the …

In the deep learning algorithm, YOLOv4 and Faster R-CNN have achieved excellent target
detection performance. To improve the real-time detection of small targets in traffic signs, this …

To solve the problem that the existing YOLOv3 algorithm had more false detections and
missed detections for traffic sign detection task with small target problems and complex …

There are three problems in YOLOv4-Tiny when it is used for traffic sign detection: the
feature pyramid network fails to fuse high-level and low-level features sufficiently, the …

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 …

Detecting traffic signs is an essential task for intelligent and connected vehicles. In this study,
a modified model based on the method of You Only Look Once (YOLO) is proposed for …

Traffic sign detection plays an important role in traffic safety and traffic management. In view
of the complex and changeable environment and detection accuracy of traffic sign detection …

Traffic sign detection is a challenging task for the unmanned driving system, especially for
the detection of multi-scale targets and the real-time problem of detection. In the traffic sign …

It is very challenging to detect traffic signs using a high-precision real-time approach in
realistic scenes with respect to driver-assistance systems for driving vehicles and …

In recent years, target detection framework based on deep learning has made brilliant
achievements. However, real-life traffic sign detection remains a great challenge for most of …