Intelligent recognition of erosion damage to concrete based on improved YOLO-v3

Concrete is one of the most common building materials in civil engineering. Buildings in Northwest China are facing strong wind erosion. Due to wind erosion, the surface of concrete peels off and erosion damage occurs, which has a very adverse impact on both the appearance of buildings and their safe use. Therefore, it is of great significance to carry out an intelligent identification of the erosion area of concrete. A deep learning dataset was established through a concrete erosion test to realize accurate recognition of erosion damage to concrete, and an improved YOLO-v3 algorithm model was proposed. Compared with other mainstream target detection algorithms, the improved version of YOLO-v3 is found to be able to achieve more accurate concrete erosion damage recognition, and the accuracy, precision, and map of the algorithm are 96.32%, 95.68%, and 75.68%, respectively, which verifies the applicability of deep learning to the research of concrete erosion damage.

Automated summary

The study focused on intelligent identification of erosion damage to concrete using deep learning, establishing a dataset and proposing an improved YOLO-v3 algorithm model. The improved YOLO-v3 algorithm shows more accurate recognition of concrete erosion damage compared to other mainstream target detection algorithms, with an accuracy, precision, and MAP of 96.32%, 95.68%, and 75.68% respectively, validating the applicability of deep learning in the research of concrete erosion damage.