A Method for Detecting Multiple Category Diseases of Steel Bridge Deck Based on YOLOv5
Due to the wide variety of types and shapes of defects in steel bridge deck detection,as well as the higher requirements compared to pavement disease detection and evaluation standards,it is nec-essary to find and train a steel bridge deck disease recognition algorithm suitable for practical steel bridge deck maintenance operations to meet the application requirements for fully automated detection of steel bridge deck defects.The multi-function detection vehicle is used to collect images of the steel bridge deck,and nine types of defects are labeled using labelimg to establish a self-made dataset.The YOLOv5 algorithm is used to complete the training of the detection model.The experimental re-sults show that the disease detection accuracy of the YOLOv5 model can reach 80%,with relatively low detection accuracy for crack-type defects and high detection accuracy for block-type defects.
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