Intelligent Recognition of Traffic Signs Based on Improved YOLO v3 Algorithm

Abstract

In recent years, assisted driving and autonomous driving technology have been paid more attention to by the public. Road sign recognition is of great practical significance for the realization of auto-driving technology. In the actual traffic environment, the traffic signs have the problems of small detectable volume, low resolution, unclear characteristics, and easy to be disturbed by the environment. In order to better realize road traffic sign recognition, this paper improves and optimizes the YOLO v3 network derived from YOLO v3 structure algorithm, enhances the data of the traffic signs by using color enhancement and other technologies, and improves the original FPN structure of the YOLO v3 network algorithm to 52 × 52. Then, the secondary sampling output characteristic diagram 108 in the YOLO v3 network is used × 108 solutions to solve these difficulties of picture size and image distortion. Use 5, 9, and 13 fixed-size pools in front of the surface of the control architecture, then the output characteristics are associated with the original characteristics of the picture so that inputs of different sizes can obtain the same output. Finally, we use the intermediate class K algorithm to group the TT100K landmark data set, reconsider the original network parameters, and compare the TT100K data set with the small target determination algorithm, such as YOLO v3 network model and improved YOLO v3 network model. The results show that compared with the traditional YOLO v3 algorithm, the optimized YOLO v3 road sign recognition algorithm has a significant improvement in sign recognition accuracy, sign recognition speed, and learning cost. When the change of FPS is very small, the recall rate and accuracy will be greatly improved. At the same time, compared with other small target detection algorithms, the improved YOLO v3 algorithm has more accurate and faster detection accuracy.

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

“…The recognition experimental results presented in Figs. 13 and 14 indicate that the MGB-YOLO algorithm effectively reduces both omission and error rates, and it provides a higher level of accuracy for small targets. These results demonstrate the effectiveness and superiority of the proposed algorithm.…”

“…Findings indicate that the algorithm not only maintains high classification precision but also considerably bolsters execution speed. Addressing the shortcomings of YOLO v3, Yang et al 13 implemented optimizations to the algorithmic architecture, the K-means clustering algorithm, and the loss function. These adjustments substantially enhanced the accuracy and speed of the detection framework, effectively mitigating issues related to low precision in road traffic sign recognition.…”

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“…YOLOv8's Enhanced Performance: J. R. [5] conducted experiments revealing that YOLOv5, YOLOv7, and YOLOv8 demonstrated a better balance across all metrics, both in validation and testing. Among all models, YOLO-NAS variations achieved the greatest recall scores, making them stand out in important applications needing high recall.. [7] proposed an optimization algorithm that, when applied to the YOLO algorithm, demonstrated improved recognition accuracy. Experiments on photos of varying resolutions showcased enhanced accuracy compared to conventional YOLO algorithms.…”

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“…Compared with traditional YOLOv3 algorithm, it has significantly improved the accuracy and speed of road sign recognition. Reference [7] detected traffic signs and improved the backbone network of YOLOv4 small model, resulting in an average accuracy increase of 3.5%, Frames per second increased by 12.5 frames per second. These models based on deep learning [8] have made progress in traffic sign detection, but are not suitable for zero movement detection.…”