Intelligent detection for industrial electrical automation based on stereo vision and feature fusion

Industrial electrical automation fault detection is a guarantee for the stable operation of industrial systems. Focusing on issues of low detection accuracy and insufficient depth perception ability in the current research, this paper establishes a multimetric stereo vision measurement model of electrical equipment, and stereo-corrects the images of electrical equipment. Then a two-channel convolutional neural network (CNN) is designed to capture global and local characteristics of the electrical equipment image. Finally, the YOLOv8 approach was adopted as the baseline model to optimize the design of the feature fusion part of the network. The recognition head network was superseded by Dyhead dynamic recognition head. The loss function part is redesigned using Focaler-IoU to solve the fitting problem caused by sample imbalance. Experimental outcome indicates that the proposed technique boosts the mean Average Precision (mAP) by no less than 5.42%, which significantly improves the detection efficiency and reliability.