基于DeepLabV3与Canny算法的路面裂缝语义分割方法
投稿时间:2022-06-30  修订日期:2022-09-11  点此下载全文
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作者单位邮编
张卫国 西安科技大学 710699
张思瑞 西安科技大学 710699
基金项目:国家自然科学基金项目 批准号:61902311项目名称:面向移动终端的低代价声波成像方法研究项目类型:青年科学
中文摘要:为准确检测混凝土路面裂缝的形态与分裂程度,避免其结构进一步损伤,提出一种改进的DeepLabV3网络语义分割模型。利用Canny算法优异的检测能力对裂缝边缘提取,改进分割网络的上采样层进行残差多层采样;优化空洞卷积的扩张率降低感受野,平衡网络对不同尺度裂缝的敏感度;融合并行注意力模块抑制分割模型易产生的伪影效应,获取更具互补性的裂缝特征。在公开数据集上进行训练与预测,在全卷积网络 (FCN)结合条件随机场(CRF)方法、Deep LabV3方法、Deep LabV3+与Lraspp方法中开展了对比实验。试验结果表明,本方法的MPA为98.73%,MIOU为87.53%,有效抑制噪声干扰,分割结果精确且连续。
中文关键词:图像处理  语义分割  裂缝检测  全卷积网络  Canny边缘检测
 
Semantic Segmentation Method of Pavement Cracks based on Deeplabv3 and Canny Algorithm
Abstract:To accurately detect the morphology and splitting degree of concrete pavement cracks and avoid further structural damage, an improved Semantic segmentation model of DeepLabV3 network was proposed. Firstly, Firstly, the crack edge is extracted by the excellent detection ability of Canny algorithm, and improve network segmentation on sampling for residual multilayer sampling. Secondly, the expansion rate of cavity convolution was optimized to reduce the receptive field and balance the sensitivity of the network to cracks of different scales. Finally, the Convolutional Block Attention Module is integrated to suppress the artifact effect easily generated by the segmentation model and obtain more complementary fracture features. The proposed method is trained and tested on open data sets, and compared with the Full Convolutional Network (FCN)combined with Conditional Random Field (CRF)algorithm, DeepLabV3 algorithm, Deep LabV3+ algorithm and Lraspp algorithm . The experimental results show that the MPA and MIOU of this method are 98.73% and 87.53% respectively, which can effectively suppress noise interference and obtain accurate and continuous segmentation results.
keywords:image processing  semantic segmentation  crack detection  full convolutional network  canny operator
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