A traffic sign recognition method based on improved YOLOv5

Qu Liguo; Zhang Xin; Lu Zibao; Liu Yuling; Chen Guohao

doi:10.12086/oee.2024.240055

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Qu L G, Zhang X, Lu Z B, et al. A traffic sign recognition method based on improved YOLOv5[J]. Opto-Electron Eng, 2024, 51(6): 240055. doi: 10.12086/oee.2024.240055

Citation:

Qu L G, Zhang X, Lu Z B, et al. A traffic sign recognition method based on improved YOLOv5[J]. Opto-Electron Eng, 2024, 51(6): 240055. doi: 10.12086/oee.2024.240055

A traffic sign recognition method based on improved YOLOv5

1.
School of Physics and Electronic Information, Anhui Normal University, Wuhu, Anhui 241002, China
2.
Anhui Provincial Engineering Research Center for Information Fusion and Control of Intelligent Robots, Wuhu, Anhui 241002, China
3.
Wuhan Mingke Rail Transit Equipment Co., Ltd., Wuhan, Hubei 430074, China

Fund Project: Project supported by Wuhu Science and Technology Plan Funding Project (2022jc07), and Anhui Provincial Department of Education Graduate Innovation and Entrepreneurship Practice Funding Project (2022cxcysj058, 2023cxcysj043)

More Information

^*Corresponding author: lgqu@ahnu.edu.cn

Received Date 07 March 2024

Revised Date 03 June 2024

Accepted Date 04 June 2024

Published Date 25 June 2024

Abstract

Abstract

Traffic sign detection is an important link in the field of autonomous driving, and given the problems of missed detections, false detections, many model parameters, and common and complex representative real environment conditions, such as poor robustness in foggy days, an improved YOLOv5 micro-target traffic sign recognition algorithm was proposed. Firstly, the dataset was atomized to adapt to the accurate identification in the foggy weather, and the PC3 feature extraction module was constructed by using a lighter partial convolution (PConv), and then the Extended Feature Pyramid Network (EFPN) was proposed in the neck network Finally, Focal-EIOU is introduced to replace CIOU as the loss function to solve the problem of false detection and missed detection of micro targets, and the CBAM attention mechanism is embedded to realize the lightweight model and significantly improves the feature extraction ability of the network model. Compared with the original YOLOv5 algorithm, the improved model is increased by 8.9% and 4.4% respectively on P and mAP0.5, the number of parameters is reduced by 44.4%, and the FPS value on NVIDIA 3080 device is 151.5, which can meet the real-time detection of traffic signs in the real scenes.
- small object detection /
- deep learning /
- YOLOv5 /
- lightweight convolution /
- EFPN /
- loss function

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References

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Overview

Overview

Traffic sign detection is a crucial component in the field of autonomous driving. Current traffic sign recognition methods face challenges such as missed detections, false positives, high model complexity, and poor robustness in these representative and complex real-world conditions, particularly under the foggy weather. To address these issues, this paper proposes an improved method for small traffic sign detection based on YOLOv5. Firstly, to tackle the problem of imbalanced sample data, the traffic sign dataset is augmented with foggy conditions to enhance the model's generalization capability. This augmentation helps the model to better handle diverse environmental conditions, improving its robustness. Secondly, a lightweight Partial Convolution (PConv) is introduced to construct the PC3 feature extraction module, replacing the C3 module in the original YOLOv5 model. This modification reduces the number of model parameters and enhances processing speed without compromising detection performance. Subsequently, an Extended Feature Pyramid Network (EFPN) is employed in the neck network, adding detection heads specifically for small objects while removing the heads for large object detection. This specialization optimizes the model's performance for small object detection. Additionally, the K-means clustering algorithm is used on the TT100K dataset to recalculate and adjust the size and ratio of anchor boxes, better accommodating small object detection. The Focal-EIOU loss function replaces the original CIOU loss function to address class imbalance and false positive issues in small object detection. Finally, a Convolutional Block Attention Module (CBAM) is embedded in the backbone network. This module performs attention operations on both channel and spatial dimensions, further enhancing the model's feature extraction capability. Compared with the original YOLOv5 algorithm, ablation experiments on the TT100K dataset show that the improved model achieves an 8.9% increase in precision (P) and a 4.4% increase in mean Average Precision (mAP0.5), with a 44.4% reduction in parameter count, reaching a frame rate (FPS) of 151.5 on NVIDIA 3080 devices. Furthermore, comparative experiments with mainstream object detection algorithms such as Faster RCNN, YOLOv7, and YOLOv8 demonstrate that the improved model achieves optimal P and mAP0.5, reaching 91.7% and 89.9% respectively, leading other models in detection accuracy. The model parameter count is 3.95M, realizing a lightweight design while maintaining high detection performance for small traffic signs. This improved model is suitable for real-time traffic sign detection in real-world scenarios, ensuring the reliable operation in autonomous driving systems.