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On Bridge Surface Crack Detection Based on an Improved YOLO v3 Algorithm

机译：基于改进的YOLO V3算法的桥面裂纹检测

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摘要

An improved bridge surface crack detection algorithm based on a further developed You Only Look Once version 3 algorithm (YOLO v3) is proposed to realize the fast and accurate detection of bridge surface cracks for timely repair application scenarios. The proposed algorithm is combined with MobileNets and convolutional block attention module (CBAM), which can detect bridge surface cracks in real time. The standard convolution is replaced by the depthwise separable convolution of MobileNets so as to reduce the number of network parameters. Moreover, in order to solve the problem of precision decline caused by depthwise separable convolution, the inverted residual block of MobileNetV2 is introduced. Furthermore, the proposed algorithm selectively learn the feature by multiplying the attention map with the input feature map through CBAM, and focus on channel and spatial attention mechanisms simultaneously. Finally, the feasibility of the algorithm is verified by experiment.
机译：基于进一步开发的桥面裂纹检测算法仅仅查看一次版本3算法（YOLO V3），建议实现用于及时修复应用场景的快速准确地检测桥接面裂缝。该算法与MobileNets和卷积块注意模块（CBAM）相结合，可以实时检测桥表面裂缝。标准卷积由MobileNets的深井可分离卷积所取代，以减少网络参数的数量。此外，为了解决深度可分离卷积引起的精度下降的问题，引入了Mobilenetv2的倒置残余块。此外，所提出的算法通过将注意力映射乘以通过CBAM乘以输入特征映射来选择性地学习该特征，并同时专注于信道和空间关注机制。最后，通过实验验证了算法的可行性。

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