A Machine Vision-Based Detection Method for Security Control of Distribution Network Engineering

In view of such troubles as too much interference factors of external environment at the construction site and the worksite supervision difficulty and so on, an improved YOLOv5 network model-based realtime detection method for distribution network engineering was proposed, and the accurate image recognition as well as the defect detection of distribution network engineering were researched. Firstly, the on-site sample data set of distribution network engineering was labeled, and the feature extraction network for YOLOv5 network was improved to speed up the multi-scale fusion and to raise the detection accuracy of small target object. On this basis, the loss function and the non-maximum suppression module were improved to raise the recognition precision of the model and to accelerate the convergence speed. Secondly, by means of Darknet deep learning model the multiple iteration training was performed to the recognition samples and the optimal weight data was saved the for test set testing. Finally, by use of TensorBoard visual tool the training and test results could be displayed. Testing results show that the average recognition accuracy of each distribution network sample can reach more than 95%, and the speed of picture recognition can reach 140 fps. Meanwhile, The improved method possesses the advantages as high detection accuracy and strong real-time performance, so it can meet the need of on-site realtime use.