摘要：

有效的道路表面裂缝检测是维护道路安全、延长道路寿命的关键。针对传统道路表面裂缝检测方法存在的难以识别细小裂缝、分割断裂以及分割精度低等问题, 提出了一种改进DeepLabv3+的道路表面裂缝检测方法, 旨在降低模型参数量的同时提高裂缝检测的准确性。首先, 使用优化后的MobileNetv2网络替换基础DeepLabv3+模型的主干网络, 以降低模型的参数量和复杂度, 提高运行速度; 其次, 将条形池化模块(SPM)融入空洞空间金字塔池化(ASPP)模块, 使得网络能够捕获到更多的裂缝上下文信息, 保留裂缝细小部分的特征; 最后, 引入卷积块注意力模块(CBAM), 使网络更加关注图像中对裂缝检测起决定作用的像素区域, 增强裂缝图像的特征表达能力。实验结果显示, 改进DeepLabv3+模型的平均像素准确率(MPA)为87.85%, 平均交并比(MIoU)为80.53%, 准确率为97.51%, 精确率为88.65%, F1值为88.24%, 相比于基础DeepLabv3+模型分别提高了1.77%、2.03%、0.30%、2.25%和1.51%, 且高于U-Net、HR-Net和PSP-Net模型。此外, 改进DeepLabv3+模型的参数量为6.382×10⁶, 是基础DeepLabv3+模型的88.3%, 实时性更好, 更适用于道路表面裂缝检测任务。

关键词: 裂缝检测, 语义分割, 卷积神经网络, 条形池化模块, 注意力机制

Abstract:

The effective detection of road surface cracks is key to maintaining road safety and prolonging road life. To address the problems of difficulty in identifying small cracks, segmentation fractures, and low segmentation accuracy for traditional road surface crack detection methods, an improved DeepLabv3+ road surface crack detection method is proposed to simultaneously reduce the number of model parameters and improve the accuracy of crack detection. First, the backbone of the DeepLabv3+ model is replaced with an optimized MobileNetv2 network to reduce the number of parameters and complexity of the model, which speeds up the operation. Second, the Strip Pooling Module (SPM) is integrated into the Atrous Spatial Pyramid Pooling (ASPP) module to enable the network to capture more crack context information and preserve the characteristics of small parts of the crack. Finally, a Convolutional Block Attention Module (CBAM) is introduced to make the network focus more on the pixel region that plays a decisive role in crack detection, which enhances the feature expression ability of crack images. According to the experimental results, the improved DeepLabv3+ model achieved a Mean Pixel Accuracy (MPA) of 87.85%, Mean Intersection over Union (MIoU) of 80.53%, accuracy of 97.51%, precision of 88.65%, and F1-Score of 88.24%; compared with the basic DeepLabv3+ model, the improvements are 1.77%, 2.03%, 0.30%, 2.25%, and 1.51%, respectively. These indices of the proposed model are higher than those of the U-Net, HR-Net, and PSP-Net models. In addition, the number of parameters of the improved model is 6.382×10⁶, which is 88.3% of that of the basis model, and the real-time performance is better, making it more suitable for road surface crack detection.

Key words: crack detection, semantic segmentation, convolutional neural network, strip pooling module, attention mechanism