基于注意力机制的多尺度道路损伤检测算法研究

Research on multi-scale road damage detection algorithm based on attention mechanism

路损伤检测是道路养护与修复的一项重要任务。现有的道路损伤检测方式以传统的人工检测为主,人工检测需要投入大量的人力和物力,检测效率低,无法适应当前道路发展的需求。进而提出了一种改进的多尺度道路损伤检测算法YOLOv8-RDD。首先,YOLOv8-RDD算法在C2f模块中使用可变形卷积(DCN)建了全新的C2f_DCN模块,扩大感受野的有效范围,更准确地定位目标对象的边界和位置,有助于提升对目标的识别和定位能力;其次,网络末端设计了全新的SPPF_GS模块,在SPPF模块中引入了自注意力机制(SA)和幻影卷积Ghost模块,并重新优化了池化核的大小,更好的处理长距离依赖性和捕获全局信息;最后,在Neck中引入坐标注意力机制(CA),强化模型的特征提取能力,减少冗余信息。实验结果表明,改进后的算法在RDD2022数据集上面的精确度(Precision)为61.1%、召回率(Recall)为55.5%,平均精度(mAP)为56.2%,相较于YOLOv8n算法分别提高了4.6%、4.7%和5.2%,在道路损伤的目标检测上取得了优异的效果。

Road damage detection is an important task in road maintenance and repair.The existing road damage detection methods primarily rely on traditional manual detection,which requires significant manpower and material resources,resulting in low detection efficiency and an inability to meet the needs of current road development.To address these problems,an improved multi-scale road damage detection algorithm,YOLOv8-RDD,was proposed.Firstly,the YOLOv8-RDD algorithm employed Deformable Convolutional Networks(DCN)in the C2f module to build a new C2f_DCN module.This expanded the effective range of the receptive field and located the boundary and position of target objects more accurately,thus enhancing the ability to identify and locate the target.At the end of backbone network,a new SPPF_GS module was designed,introducing the Self-Attention(SA)mechanism and the Phantom Convolution Ghost module into the SPPF module,with the size of pooled kernel re-optimized to better deal with long-distance dependence and capture global information.Finally,Coordinate Attention(CA)was introduced into the Neck to strengthen the feature extraction ability of the model and reduce redundant information.Experimental results demonstrated that the improved algorithm achieved a Precision of 61.1%,a Recall rate of 55.5%,and a mean average precision(mAP)of 56.2%on the RDD2022 dataset.Compared with the YOLOv8n algorithm,the results were improved by 4.6%,4.7%,and 5.2%,respectively,which achieved excellent performance in the target detection of road damage.