基于连续注意力机制和卷积金字塔的路面裂缝检测
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南京邮电大学自然科学基金(NY220057)


Pavement Crack Detection with Continuous Attention Mechanism and Convolution Pyramid Structure
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    摘要:

    近年来, 随着全球汽车保有量的增加和路面的不断扩建, 路面裂缝检测受到了广泛的关注. 虽然许多裂缝检测器模型已经被提出, 但也存在一些问题, 例如: 一些宽度较细的裂缝可能未被检测而出现裂缝断裂的现象; 边缘信息可能会在过滤或池化过程中丢失. 本文以SegNet为基础框架, 编码层设计了一种连续注意力机制, 并且在特征图通过解码层之前添加了卷积金字塔结构, 以减少裂缝检测中的断裂, 获得更完整的边缘信息. 与相关方法相比, PrecisionRecallF1-measure三个指标分别提高了2.47%、8.21%和6.87%, 对Crack200、Crack500和CrackForest三个开源数据集检测结果的平均交并指标(MIoU)提高了14.35%.

    Abstract:

    With the increase in global vehicles and the expansion of road surfaces, pavement crack detection has received extensive attention in recent years. Many detector models have been proposed, with some problems, though. For example, some narrow cracks may not be detected, leading to discontinuous cracks; the detailed crack edge information may be lost during filtering or pooling. On the basis of SegNet, a continuous attention mechanism is designed in the encoder layer, and a convolutional pyramid structure is added before the feature map passes through the decoder layer to reduce the fracture in crack detection and obtain more complete edge information. The Precision, Recall, and F1-measure of our approach are 2.47%, 8.21%, and 6.87% higher than those of the related method, respectively, and the Mean Intersection over Union (MIoU) of the detection results on the three open datasets, namely, Crack200, Crack500, and CrackForest is improved by 14.35%.

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陈良全,王彩玲,刘华军,蒋国平.基于连续注意力机制和卷积金字塔的路面裂缝检测.计算机系统应用,2021,30(8):249-255

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  • 收稿日期:2020-11-24
  • 最后修改日期:2020-12-22
  • 在线发布日期: 2021-08-03
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