The effective identification of micro cracks is of great significance to the early fault diagnosis of structures. The image segmentation method and other methods are difficult to achieve satisfied results in the detection of micro cracks with complex shapes and broken area. Therefore, transforms the problem of micro cracks identification into a series of dense and continuous central point prediction. A feature extractor is established by using the refined layered residual module, and the feature reuse attention module is also utilized to propose a micro cracks detection method. Firstly, the same rectangular bounding box is used to label the crack track densely and continuously. Secondly, the ablation experiments are implemented on the different refined hierarchical residual module to obtain the backbone network which is conducive to the feature extraction of micro cracks. Finally, six different feature reuse methods are compared by combining the attention module with feature reuse and backbone network. Experimental results show that the highest and average accuracy of the proposed method are 61. 0% and 54. 7% , respectively, which are 4. 9% and 6. 3% higher than the original model. The proposed method successfully identifies the micro cracks and their local broken areas, and suppresses background interference in practical application.