Pavement Disease Detection Algorithm Focusing on Shape Features

doi:10.3778/j.issn.1002-8331.2404-0259

Abstract

Abstract: Automatic pavement disease detection is a crucial technology for achieving intelligent road management. In addressing the challenges posed by small disease targets in pavement images, significant variations among different types of diseases, and complex background environments, an algorithm named FSF-YOLO （focusing on shape features YOLO） is proposed, which is based on the YOLOv8 architecture. This algorithm incorporates an enhanced feature extraction module designed to retain multi-dimensional spatial feature information, thereby enhancing the backbone network’s capability to extract features from low-resolution images and small disease targets. Additionally, it introduces a deformable attention feature fusion module that leverages the elongated shape features of diseases to expand the target recognition area and improve the model’s feature expression ability for long distance disease targets. Furthermore, the algorithm utilizes a grouped convolution space pyramid pool module to bolster the recognition of disease targets of varying sizes. Lastly, it employs lightweight shared convolutional detection heads to reduce both the number of network parameters and the computational load. Experimental results demonstrate that the proposed method offers superior performance in detecting various types of pavement diseases, with an average accuracy of 67.3% on the RDD2022 dataset, which is a 5.3 percentage points improvement over the original algorithm.

Key words: pavement distress detection, shape features, deformable attention, grouped convolution space pyramid, YOLOv8

摘要： 路面病害自动化检测是实现道路智慧化管养的关键技术之一，针对路面病害图像中病害目标占比小、不同类型病害尺度差异大、背景环境复杂等特性，基于YOLOv8架构，提出聚焦形状特征的路面病害检测算法FSF-YOLO（focusing on shape features YOLO）。构建一种无信息丢失的加强特征提取模块，通过保留多维度空间特征信息，增强骨干网络对低分辨率图像和细小病害目标的特征提取能力；引入可形变注意力特征融合模块，利用病害细长形状特征拓展目标识别区域，提高模型对于长距离病害目标的特征表达能力；运用分组卷积空间金字塔池化模块，强化不同尺寸病害目标特征识别；采用轻量级共享卷积检测头，减少网络参数量和计算量。实验结果表明，提出的方法对不同类别的路面病害目标均获得了较好的效果，在RDD2022数据集上的平均精度达到67.3%，与原算法相比提升了5.3个百分点，整体性能优于其他路面病害检测算法。

关键词: 路面病害检测, 形状特征, 可形变注意力, 分组卷积空间金字塔, YOLOv8

DENG Tianmin, CHEN Yuetian, YU Yang, XIE Pengfei, LI Qingying. Pavement Disease Detection Algorithm Focusing on Shape Features[J]. Computer Engineering and Applications, 2024, 60(24): 291-305.

邓天民, 陈月田, 余洋, 谢鹏飞, 李庆营. 聚焦形状特征的路面病害检测算法[J]. 计算机工程与应用, 2024, 60(24): 291-305.

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