激光与光电子学进展, 2020, 57 (14): 141031, 网络出版: 2020-07-28   

复杂背景下交错低秩组卷积混合深度网络的路面裂缝检测算法研究 下载: 1087次

A Novel Pavement Crack Detection Algorithm Using Interlaced Low-Rank Group Convolution Hybrid Deep Network Under a Complex Background
作者单位
长安大学电子与控制工程学院, 陕西 西安 710064
摘要
由于混凝土路面光照强度不均匀、背景复杂、噪声干扰大,传统的裂缝检测算法难以准确提取其裂缝特征。为了在提高裂缝检测准确性的同时减少计算冗余,提出了一种将低秩核和组卷积结合的交错低秩组卷积混合深度网络的路面裂缝检测算法。首先利用重叠滑动窗口裁剪方法建立裂缝图像数据集,在训练集上生成一个具有较好鲁棒性的分类器,对裂缝及非裂缝图像进行分类,然后采用自适应阈值法得到边缘轮廓清晰的裂缝二值化图像,最后采用中轴线法求取裂缝最大宽度。在测试集上验证模型的性能,实验结果表明测试精度为0.9726,效果优于经典的裂缝检测算法,而且相对于卷积神经网络及其变体大幅减少了模型参数,处理图像的速度达到了每秒14张,并且在三个公开数据集上都达到了较好的检测效果。在2.5 mm以上的裂缝宽度上,计算相对误差小于0.02,较好地达到了工程实际要求。
Abstract
Accurately extracting the crack characteristics using the traditional crack detection algorithm is challenging owing to the uneven light intensity, complex background, and significant noise interference of concrete pavement. Herein, to improve crack detection accuracy and reduce computational redundancy, a pavement crack detection algorithm was proposed that used an interlaced low-rank group convolution hybrid deep network combined with low-rank kernel and group convolution. First, crack image datasets were established using the overlapping sliding window clipping method. A robust classifier was generated on the training set to classify crack and no-crack images. Then, the adaptive threshold method was used to obtain a crack binary image with clear edge contours. Moreover, the central axis method was used to achieve the maximum width of the crack. The performance of the model was verified on the testing set. Experimental results show that the detection accuracy is 0.9726, thus showing an improvement over the traditional crack detection algorithm. Compared with the convolutional neural network and its variants, the proposed model involved a significantly reduced set of parameters. Images were processed at 14 frames per second, and good detection results were achieved on three public datasets. For crack widths greater than 2.5 mm, the relative error of the calculation is less than 0.02, which complies with practical engineering requirements.

李刚, 刘强伟, 万健, 马彪, 李莹. 复杂背景下交错低秩组卷积混合深度网络的路面裂缝检测算法研究[J]. 激光与光电子学进展, 2020, 57(14): 141031. Gang Li, Qiangwei Liu, Jian Wan, Biao Ma, Ying Li. A Novel Pavement Crack Detection Algorithm Using Interlaced Low-Rank Group Convolution Hybrid Deep Network Under a Complex Background[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141031.

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