Ground Marker Line Detection Algorithm Based on ResNet18-YOLOv8n
Ground marker detection plays an important role in autonomous driving and traffic scenario analysis,which is essential for road safety and road intelligence.However,the traditional sign line detection algorithm has the problems of low detection accuracy and few studies on the detection of traffic arrow markers.In order to solve such problems,an improved traffic sign recognition algorithm based on YOLOv8n was proposed.Improvements include replacing the backbone network of the YOLOv8n model with the ResNet-18 network in the Timm model library to improve the accuracy of image recognition.The GIoU boundary loss function is used to replace the original CIoU loss function to improve the regression performance of the bounding box and further improve the detection efficiency and accuracy.In the experiment,2 099 ground marker line images in the CeyMo dataset were trained and evaluated.Experimental results show that the original YOLOv8n model has a precision of 82.2%and mean average precision of 98%,while the optimized model achieves a precision of 88.1%and mean average precision of 99.3%,which improves the precision of the model by 5.9%and the average precision by 1.3%,respectively.Comprehensive analysis shows that after the introduction of ResNet-18 Backbone network and GIoU loss function,it not only improves the detection efficiency but also improves the recognition precision,and is significantly better than the YOLOv5s and YOLOv8n algorithms,with higher effectiveness and detection precision.
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