基于改进Faster-RCNN的沥青路面裂缝检测
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2020年度陕西省交通运输厅科研项目(20-24K)


Crack Detection of Asphalt Pavement Based on Improved Faster-RCNN
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    摘要:

    针对目前沥青路面裂缝检测存在的识别率低和细微裂缝在复杂背景下难以检测的问题, 提出了基于改进Faster-RCNN的裂缝检测方法. 首先, 通过多功能路面检测车采集路面图像, 将13 000张图片按8:2的比例分为训练集和测试集来建成路面裂缝检测数据集; 然后分别采用VGG16、MobileNet-V2和ResNet50网络替换Faster-RCNN模型中的特征提取网络对裂缝进行识别, 结果表明, ResNet50与Faster-RCNN结合对裂缝的检测准确率达到0.805 8, 效果最好; 裂缝都分布在同一水平面上, 不存在层次信息, 因此将ResNet系列其它网络与Faster-RCNN模型结合, 以期得到更好的检测效果, 结果表明, 相比于ResNet18和ResNet101, 还是ResNet50检测性能最好; 由于还存在细微裂缝漏检的问题, 将CBAM模块引入ResNet50, 并且比较不同插入位置对检测准确率的影响. 实验表明, 改进的Faster-RCNN模型检测精准度达到85.64%, 能有效检测出复杂背景下的细微裂缝.

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    Given the low recognition rate and the difficulty in detecting small cracks in the asphalt pavement under complex background, the crack detection method based on improved Faster-RCNN is proposed. First, the road surface images are collected by the multifunctional road detection vehicle, and 13 000 pictures are divided into training sets and test sets at a ratio of 8:2. Then VGG16, MobileNet-V2, and ResNet50 networks are utilized to replace the feature extraction network in the Faster-RCNN model to identify the cracks. The results show that the combination of ResNet50 and Faster-RCNN can achieve the best result with an accuracy of 0.805 8. The cracks are distributed on the same level without hierarchical information. Therefore, other ResNet networks are expected to work better with the Faster-RCNN model. However, it turns out that ResNet50 still outperforms ResNet18 and ResNet101. In the case of missed detection of small cracks, the convolutional block attention module (CBAM) module is also introduced into ResNet50 and the influence of different insertion positions on the detection accuracy is compared. Experiments show that the improved Faster-RCNN model has a detection accuracy of 85.64%, which can effectively detect small cracks under complex backgrounds.

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徐康,马荣贵.基于改进Faster-RCNN的沥青路面裂缝检测.计算机系统应用,,31():1-8

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  • 收稿日期:2021-10-21
  • 最后修改日期:2021-11-18
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  • 在线发布日期: 2022-03-09
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