Aiming at the problems of low detection accuracy and false detection and missed detection of small arcing in the existing pantograph arcing detection model, a lightweight pantograph arcing detection algorithm RIL-YOLO based on improved YOLOv8 is proposed. Firstly, combined with RepConv module and GhostNet idea, a lightweight feature extraction module RELAN is designed to reduce the amount of parameters and calculations while maintaining the performance of the model for arc feature extraction. Secondly, aiming at the problem of small arc missed detection, a small target detection module is added, and a weighted bidirectional feature pyramid network structure is used to achieve a higher level of feature fusion, so as to improve the detection ability of the model for small targets. In order to solve the problem that the computational cost of the small target detection module is greatly increased, the neck network is reconstructed, the reconstructed IBiFPN structure only increases the computational complexity by 0.3G while ensuring the accuracy of the model. Finally, a lightweight detail enhancement detection head is designed to replace the YOLOv8 detection head, which improves the model ′s ability to capture detailed features while reducing model parameters. The research results show that compared with the YOLOv8n model, the RIL-YOLO model has an average accuracy of AP@0.5 and AP@0.5:0.95 increased by 5.2% and 3.7%, respectively, when the number of model parameters is reduced by 66% and the calculation amount is reduced by 13.6%. The detection speed reaches 112.4 fps,which can effectively realize rapid and accurate detection of ignition arc. The method provides theoretical method reference for real-time detection of pantograph arc.