Underwater pier disease identification method based on deep learning and sonar imaging

Abstract

The invention provides an underwater pier disease identification method based on deep learning and sonar imaging, and the method comprises the steps: obtaining a prepared underwater pier scanning picture through employing underwater side sonar equipment, wherin the underwater pier scanning picture comprises a disease and no-disease picture; increasing the number of data sets by using an image enhancement method; marking the data set, marking a disease area with a rectangular frame, and storing coordinate information; dividing the data set into a training test set, a verification set and a testset; establishing a yolov3 model in the deep learning target detection network, and performing training to obtain a training model; and controlling side sonar equipment to scan along the underwater pier part on the water surface to obtain a scanning picture, and performing automatic underwater pier disease identification by using the trained yolov3 model. The method is high in efficiency and lowin cost, and has more obvious advantages of automation and real-time performance compared with a traditional manual diving method and a sonar manual screening method.

Description

technical field

[0001] The invention relates to the field of interaction technology between civil engineering and artificial intelligence, in particular to a method for identifying underwater pier diseases based on deep learning and sonar imaging. Background technique

[0002] The bridge pier is the main load-bearing component of the bridge pier, and most of the load of the bridge structure is transmitted to the foundation through the pier. Any loss of bearing capacity of a bridge pier will lead to the overall instability and destruction of the bridge pier. Therefore, the safety of the bridge pier must be highly valued. The underwater part of the bridge pier has been subjected to harsh environments such as erosion and corrosion for a long time, which may cause defects such as defects, cracks, and exposed ribs or even damage underwater, which will seriously affect the service life and even the bearing capacity of the bridge. At present, the detection methods of underwater pi...

Images