Detection method of small foreign matter in transformer based on small target enhancement and contrast learning

At present, the use of robots to carry out transformer internal inspection work has been related research, if the intelligent detection of small targets such as foreign bodies and small discharge traces inside the transformer can be realized, the efficiency of robot internal inspection will be greatly improved. For small target detection, the current popular method in the industry is to improve the detection accuracy by optimizing the structure of the network model, but the disadvantage is that it increases the difficulty of the algorithm design and the computational complexity. In this paper, based on the Faster-RCNN model, small target enhancement and contrast learning methods are proposed for small target detection in the industrial field under the premise of ensuring the detection accuracy of large-scale targets. The experimental results on the transformer internal inspection data set show that our proposed method is superior to the existing methods. It provides a new solution to the problem of improving the recognition effect of small targets.