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基于改进YOLOv8的交通标识检测方法

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近年来,上海开展新型基础测绘试点工作,已完成全市上万千米的全息道路,覆盖了上海城市主要道路.随着智能驾驶的快速发展,准确地检测和识别道路交通标识是构建智能驾驶道路框架数据的重要一环.在实际场景中很多因素会给影像中交通标识的检测带来挑战,如运动模糊、日照条件及拍摄角度等.针对此问题,本文提出了一种基于YOLOv8改进的交通标识检测算法.在模型的Neck部分融合GAM注意力机制,增强了交通标识的特征信息;使用Wise_IoU损失函数代替原有的损失函数,提升了数据集的训练性能.与未作任何优化的模型相比,优化后的模型在交通标识检测上的精确度和平均精度均值分别提升了6.5%、4.1%,具有实际应用价值.
Traffic signs detection algorithm based on improved YOLOv8
It has carried out pilot work on new fundamental surveying and mapping,and has completed more than 10 000 km of holographic roads in the city in recent year,covering the main roads in Shanghai.With the rapid development of intelligent driving,accurate detection and identification of road traffic signs is essential in constructing intelligent driving road framework data.In actual scenarios,many factors will bring challenges to the detection and recognition of traffic signs,such as motion blur,sunlight conditions,and shooting angles.So,the paper proposes an improved traffic signs detection algorithm based on YOLOv8.The GAM attention mechanism is introduced in the Neck part of the model to enhance the characteristic information of traffic signs.The Wise_IoU loss function has improved the training performance of the dataset compared to the original loss function.Compared with the model without any optimization,the accuracy and mean average precision increased by 6.5%and 4.1%respectively,which has practical application value.

high-precision mapintelligent drivingYOLOv8attention mechanismloss functiontraffic sign detection

李玉婷、袁振超、张丽

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上海市测绘院,上海 200063

自然资源部超大城市自然资源时空大数据分析应用重点实验室,上海 200063

高精地图 智能驾驶 YOLOv8 注意力机制 损失函数 交通标识检测

2024

测绘通报
测绘出版社

测绘通报

CSTPCD北大核心
影响因子:1.027
ISSN:0494-0911
年,卷(期):2024.(12)