基于注意力解码和连续性监督的裂缝检测方法OA北大核心

Crack detection is an important measure to prevent building collapse accidents.Aiming at the weak crack fea-ture and interference from noise in crack images,encoder-decoder nets in semantic segmentation are introduced to improve the detection robustness.An attention decoder module which enhances the attention of the crack feature in decoder through attention mechanism is designed to address the shortcomings of the simple decoder in traditional encoder-decoder nets and the low efficiency of connecting the semantic information in encoding features.For the problem of cracks frac-ture in predictions,the continuous supervision algorithm is improved,by adding adjacency connected predictions and combining the corresponding loss function for supervision,the continuity of crack features in prediction results is improved.Combining these two methods,an automatic encoder-decoder crack detection model is proposed.The experimental results on two datasets verify the superiority of the proposed model,the best detection accuracy is achieved among the commonly used models selected.