基于改进YOLOv5s的路面病害检测
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山西省重点研发计划(202102010101002, 202202110401015); 中北大学研究生科技立项(20231931)


Pavement Disease Detection Based on Improved YOLOv5s
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    摘要:

    针对路面病害检测中因病害形态多样、尺度差异大、背景灰度值相似而导致检测精度较低的问题, 提出一种改进的轻量化路面病害检测模型PD-YOLOv5s (pavement disease-YOLOv5s). 首先, 模型嵌入三维无参数注意力机制SimAM, 在不额外增加模型参数的同时有效增强模型在复杂环境下的特征提取能力; 其次, 引入残差块Res2NetBlock增加模型感受野, 增强模型在更细粒度层次上的特征融合能力. 最后, 构建SPD-GSConv模块完成下采样, 从而有效捕捉不同尺度的目标特征, 将提取的特征融入模型完成路面病害分类检测. 在真实路面病害数据集上实验结果表明: 相较于原YOLOv5s, PD-YOLOv5s模型平均精度值(mAP)提升4.7%, 参数量降低至6.78M, 检测速度达到53.97 f/s. PD-YOLOv5s在降低网络计算成本的同时具有优越的检测性能, 对路面病害检测具有工程应用价值.

    Abstract:

    This study proposes an improved lightweight pavement disease detection model called pavement disease-YOLOv5s (PD-YOLOv5s) to address the problem of low detection accuracy in pavement disease detection due to diverse disease forms, large-scale differences, and similar background grayscale values. Firstly, the model applies a three-dimensional parameter-free attention mechanism called SimAM to effectively enhance the feature extraction ability of the model in complex environments without increasing the number of model parameters. Secondly, the model integrates the residual block Res2NetBlock to expand its receptive field and improve its feature fusion at a finer granularity level. Finally, the SPD-GSConv module is constructed for downsampling to effectively capture target features of different scales and integrate the extracted features into the model to perform pavement disease classification detection. Experimental results on real pavement disease datasets show that the mean average precision (mAP) of the PD-YOLOv5s model is improved by 4.7% compared to that of the original YOLOv5s model. The parameters of the proposed model are reduced to 6.78M, and the detection speed reaches 53.97 f/s. The PD-YOLOv5s model has superior detection performance while reducing network computing costs, making it valuable for engineering applications in pavement disease detection.

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高晶,姚金杰,刘鹏杰,郭钰荣,王鸿儒,马文.基于改进YOLOv5s的路面病害检测.计算机系统应用,2024,33(9):253-260

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  • 收稿日期:2024-03-06
  • 最后修改日期:2024-04-10
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  • 在线发布日期: 2024-07-24
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