基于加强特征提取的道路病害检测算法

Road damage detection algorithm based on enhanced feature extraction

针对道路病害区域小、类别数量不均衡导致检测困难的问题,提出基于YOLOv7-tiny的道路病害检测算法RDD-YOLO。首先,采用K-means++算法得到拟合目标尺寸更好的锚框。其次,在小目标检测支路上使用量化感知重参数化模块(QARepVGG),增强浅层特征提取,同时构建加强注意力模块(AM-CBAM)嵌入颈部的3个输入,抑制复杂背景干扰。然后,设计特征融合模块(Res-RFB),模拟人眼扩大感受野融合多尺度信息,提高表征能力;另外,构造轻量级解耦头(S-DeHead)提高小目标检测精确率。最后,采用归一化Wasserstein距离度量(NWD)优化小目标定位过程,并缓解样本不均衡问题。实验结果表明,与YOLOv7-tiny相比,RDD-YOLO算法在仅增加0.71×10^(6)参数量和1.7 GFLOPs计算量的成本下,mAP50提高6.19个百分点,F1-Score提高5.31个百分点,并且检测速度达到135.26 frame/s,满足道路养护工作中对检测精度和速度的需求。

In response to the challenge posed by the difficulty in detecting small road damage areas and the uneven distribution of damage categories,a road damage detection algorithm termed RDD-YOLO was introduced based on the YOLOv7-tiny architecture.Firstly,the K-means++algorithm was employed to determine anchor boxes better conforming to object dimensions.Subsequently,a Quantization Aware RepVGG(QARepVGG)module was utilized within the auxiliary detection branch,thereby enhancing the extraction of shallow features.Concurrently,an Addition and Multiplication Convolutional Block Attention Module(AM-CBAM)was embedded into the three inputs of the neck,effectively suppressing disturbances arising from intricate background.Furthermore,the feature fusion module Res-RFB(Resblock with Receptive Field Block)was devised to emulate the expansion of receptive field in human visual perception,consequently fusing information across multiple scales and thereby amplifying representational aptitude.Additionally,a lightweight Small Decoupled Head(S-DeHead)was introduced to elevate the precision of detecting small objects.Ultimately,the process of localizing small objects was optimized through the application of the Normalized Wasserstein Distance(NWD)metric,which in turn mitigated the challenge of imbalanced samples.Experimental results show that RDD-YOLO algorithm achieves a notable 6.19 percentage points enhancement in mAP50,a 5.31 percentage points elevation in F1-Score and the detection velocity of 135.26 frame/s by only increasing 0.71×10^(6)parameters and 1.7 GFLOPs,which can meet the requirements for both accuracy and speed in road maintenance.