METHOD FOR IDENTIFYING CONCRETE CRACKS BASED ON YOLOV3 DEEP LEARNING MODEL

摘要

#$%^&*AU2020101011A420200723.pdf#####ABSTRACT The present invention belongs to the technical field of concrete structure damage detection, and discloses a method for identifying concrete cracks based on a YOLOv3 deep learning model. Crack images are imported into the YOLOv3 model and automatically compressed to a 416x416 pixel resolution; the original images are each divided into SxS grids according to the scale of a feature map by up-sampling and feature fusion methods similar to FPN; an Intersection over Union (IoU) of a candidate bounding box and a ground truth bounding box is taken as an evaluation standard, and all crack target annotation boxes in an image training set are subjected to K-means clustering analysis to obtain the size of the candidate bounding box; and a probability that each bounding box contains targets is predicted through logistic regression. The present invention simplifies the complexity of network training and reduces the computing cost, quickly and accurately identifies multiple targets, has a much better accuracy rate than other models while quickly detecting the targets, has stronger robustness and generalization capability, and is more suitable for engineering application environment. 14