Yolo-Based Traffic Sign Recognition Algorithm

Abstract

With the rapid development of intelligent transportation, more and more vehicles are equipped with intelligent traffic sign recognition systems, which can reduce the potential safety hazards caused by human cognitive errors. Therefore, a more safe and reliable traffic sign recognition system is the demand of drivers, and it is also the research hotspot of current automobile manufacturers. However, the pictures taken by the actual driving car are inevitably distorted and blurred. In addition, there are external uncontrollable factors, such as the impact of bad weather, which make the research of traffic sign recognition system face many difficulties, and the practical application is far from mature. In order to solve the above challenges, this paper proposes a Yolo model for traffic sign recognition. Firstly, the traffic signs are roughly divided into several categories and then preprocessed according to the characteristics of various types of signs. The processed pictures are input into the optimized convolutional neural network to subdivide the categories to obtain the specific categories. Finally, the proposed recognition algorithm is tested with the data set based on the German traffic sign recognition standard and compared with other baseline algorithms. The results show that the algorithm greatly improves the running speed on the basis of ensuring a high classification accuracy and is more suitable for traffic sign recognition system.

Real-time detection of road manhole covers with a deep learning model

“…Francies et al 11 validated the application of the YOLO algorithm for the detection and recognition of multiple 3D objects, substantiated through experimentation on the Pascal VOC dataset. Li et al 12 equipped a vehicle with an intelligent traffic sign recognition system to mitigate potential safety risks engendered by human cognitive inaccuracies. They proposed a YOLO-based model specifically for traffic sign recognition.…”

“…mAP is specifically designed for multitarget detection and can be represented as Eq. (12) In Eq. ( 12), N represents the total number of samples present in the validation set.P(k) indicates the precision achieved when k targets are simultaneously detected, while �R(k) represents the change in recall as the number of detected samples transitions from k-1 to k. Additionally, C denotes the number of classes considered by the model.…”

Research on intelligent vehicle object detection algorithm based on DC-YOLOv8s

“…But in terms of detection accuracy is still not ideal. Li et al [11] divided various traffic signs into several categories according to their characteristics. Then the pre-processed target is detected and identified, and the running speed is significantly improved.…”

Realtime Vehicle Tracking Method Based on YOLOv5 + DeepSORT

“…Te increasing number of vehicles has caused great difculties in trafc management. Vehicle tracking is an application of a target tracking in the feld of transportation, which can serve to alleviate the pressure of trafc management [1][2][3]. At present, the mainstream target tracking method is the discriminative tracking method, which adds the step of target detection and makes the tracking more accurate.…”