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Vehicle-Mounted Traffic Sign Detection Algorithm
Based on Faster-YOLOv8 Network Model
GAO Liangpeng, ZHAO Bowen, JIAN Wenliang
2024, 43(8):
114-123.
DOI: 10.3969/j.issn.1674-0696.2024.08.14
Traffic sign detection is one of the crucial tasks in applications such as autonomous driving, intelligent traffic systems, and road safety monitoring. In response to the problems such as a large number of small target objects, low accuracy, large volume of tradition models and unsuitable deployment in traffic sign detection, a novel traffic sign detection algorithm based on YOLOv8n network model, that is Faster-YOLOv8, was proposed. In the Neck section, the proposed model optimized the network structure of YOLOv8n by employing the C2f-Faster module (efficient fusion of C2f and FasterNet), which reduced the number of model parameters and model size. Furthermore, EMA attention mechanism was introduced to the backbone network of the model to realize better multi-scale and spatial perception, which improved feature extraction of the model. Additionally, a small target detection layer was added to effectively combine feature information from different scales and preserve more detailed information, thereby enhancing the detection ability of small objects. Finally, SIoU was utilized as the boundary loss function to improve detection accuracy. The research results demonstrate that the improved Faster-YOLOv8 achieves detection accuracy (Precision) , recall rate (Recall), and mean average precision (mAP@0.5) of 79.8%, 69.3%, and 77.8%, respectively, in the Chinese traffic sign detection dataset TT100K. Compared to the YOLOv8n model, it exhibits an improvement of 1.1%, 2.8%, and 2.9% in these metrics, while reducing model parameters and size by 23.59% and 19.16%, respectively. The proposed model significantly enhances both detection accuracy and model lightweighting, demonstrating practical utility superior to the existing methods.
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