Research And Application Of The Transmission Line Pin Defect Detection Based On The Multi-Level Cascading Structure

With the launch of the 14th Five-Year Plan,a stable power supply has become a key part of ensuring the normal operation of the economy and society.The pin is a device used to fix the nut in the transmission line.The fall of the pin will cause the instability of the transmission line and easily cause a trip accident.In recent years,the object detection method based on the deep neural network has been developed rapidly,especially in the power operation and maintenance combined with UAV inspection,which improves the inspection efficiency and personal safety of inspectors.Therefore,a pin defect detection method based on the deep neural network has important research significance and application value for the inspection personnel to complete the pin defect inspection work,and to maintain power transmission safety.There are three main challenges in the pin defect detection.The biggest challenge is that pins are small objects in an absolute sense;secondly,the complex natural environment background and a large number of similar mechanical parts in the vicinity increase the difficulty of detection;thirdly,there exists the class imbalance problem between the normal status pins and missing status pins.Current research on the pin defect detection is divided into detection methods based on traditional image processing technology and the object detection model methods based on deep neural networks,but the performance of detecting pin defects still cannot meet the actual needs of the industry.In this paper,a method of pin defect detection based on multi-level cascading structure is introduced.Its performance is better than that of using single deep neural network object detection model directly to detect pin defect.The detection method in this paper is divided into four modules,which are image preprocessing module,redundant sliding window segmentation module,pin image positioning module and pin state classification module.By means of the multi-level cascading,not only the proportion of the pin image in the image to be detected is gradually increased,the features of the pin image are magnified,but also the unrelated complex background can be filtered out.In order to train and test the pin defect detection method proposed in this paper,three pin sample data sets based on real transmission line scenarios are constructed.In the construction process,the problem of class imbalance in the pin sample data set is improved by using the method of data enhancement.A large number of experiments have been carried out on the data set of the proposed method and its important modules.The experimental results show that the proposed method has a high accuracy in the detection of missing pins,and its effectiveness has been verified in the ablation experiment of the improved part.In addition,this paper designs and implements a pin defect detection system for inspection personnel.The system uses Python language to develop,using B/S architecture,front and rear end separation of development.Vue and other technologies are used in the front end to realize the interface of uploading the inspection map,calling the deployed pin defect detection method and viewing the test results of pins.At the back end,Flask was selected as the Web framework,the relational database MySQL was used to store the patrol maps and inspection results,and the pin defect detection model deployed via Paddle ad-serving was invoked using the gRPC.