基于注意力机制的深度学习路面裂缝检测

为实现自动准确地检测路面裂缝,提升路面裂缝检测效果,提出了一种基于注意力机制的裂缝检测网络(attention-based crack networks,ACNet).该网络采用编码器-解码器网络构架,编码器采用ResNet34为骨干网,提取路面裂缝特征;在编码器和解码器间加入基于注意力机制的特征模块(attention-based feature module,AFM),以利用全局信息和增加对检测不同尺度裂缝的鲁棒性,更好地提取裂缝特征和定位裂缝位置;在解码阶段也引入注意力机制,设计了基于注意力机制的解码模块(attention-based decoder module,ADM),实现对裂缝的准确定位.在公共裂缝数据集CFD和CRACK500上,与U-Net等其他8种方法进行了比较,结果表明,ACNet裂缝检测效果更理想,在主观视觉上,裂缝定位更准确,细节更丰富;在实验指标F1和重合率上,检测结果都有明显提升,说明了该网络的有效性.

Pavement Crack Detection with Deep Learning Based on Attention Mechanism

To detect pavement crack automatically and accurately and improve detection effect,an attention-based crack network(ACNet)was proposed.ACNet adopts the encoder-decoder structure,and the encoder uses ResNet34 as the backbone network to extract pavement crack features.An attention-based feature module(AFM)is added between the encoder and the decoder,which utilizes the global information and increases the robustness of detecting different scales cracks,and it contributes to extract crack features and locate crack positions.The attention mechanism is also introduced in the decoding stage,and an attention-based decoder module(ADM)is designed to improve accuracy of detecting crack.Extensive experiments on two public crack datasets CFD and CRACK500 show that ACNet has better detection performances than other 8 methods,on the subjective vision,the crack location is more accurate and the details are more abundant;the experimental indicators F1 score and overlapping rate are significantly improved,which demonstrates the effectiveness of the proposed method.