Research on Infrared Image Detection Algorithms for Foreign Objects in WPT Systems

This paper proposes an improved YOLOv5 deep learning algorithm to efficiently detect foreign objects in wireless power transmission (WPT) systems based on infrared images, in response to the presence or dropping of metallic foreign objects in wireless charging systems. First, the model is made lighter and the detection speed is improved by replacing the backbone network CSPDarkNet53 with MobileNetv3. At the same time, the ECA attention mechanism is added to make the network pay more attention to the parameter relationship information and localization information of the model, improving the feature extraction capability of the network. Secondly, the original loss function CIoU of the network is replaced with WIoU, which makes the model converge better and improves the precision of the algorithm detection. Finally, the improved YOLOv5 model was validated on the collected infrared image datasets for the detection of metallic foreign objects, with an increase in precision from 96.2% to 98.3%, recall from 96.5% to 98.8%, overall mAP to 99.5%, and the detection speed improved by nearly 15%, successfully enabling the detection of foreign objects in wireless power transmission systems via infrared images.