Icing detection on ADSS transmission optical fiber cable based on improved YOLOv8 network

Icing detection promptly on all-dielectric self-supporting (ADSS) optical fiber cable on overhead transmission lines is very difficult, so it presents an icing detection method based on the improved YOLOv8 network in this paper. Initially, the improved YOLOv8 network is used to detect and extract the regions of ADSS optical fiber cable icing, reducing interference from complex background. The introduction of the GE attention module in the YOLOv8 network enhances its adaptability to various environments, and improves the recognition of icing states in different scenarios. The BiFPN feature fusion module is incorporated to replace the concatenate structure in the original YOLOv8 network, in order to improve detection accuracy. Subsequently, based on RCF edge detection algorithm, icing optical fiber cable edge feature is obtained in the RGB images. Finally, the icing thickness is computed based on pre-icing image. The experimental results showed that compared to the original YOLOv8 network, this improved network improves the accuracy of icing detection by 5.9%, the recall rate by 5%, and the average accuracy by 2.2%. The algorithm achieved the detection speed of 147 FPS, and an error rate of 2.5% in the icing thickness detection, which provided a valuable reference for ADSS optical fiber cable monitoring and ensuring the normal operation of the power signal network, as well as for early warning of icing on ADSS optical fiber cables.