The　performance　of　modern　railway　transit　systems　heavily　relies　on　the　overhead　power　system　(OPS).　However,　OPSs　built　on　the　ground　are　vulnerable　to　foreign　objects　that　can　pose　a　threat　to　railway　safety.　In　this　paper,　an　improved　lightweight　detection　method　called　OPS-YOLO,　which　is　based　on　YOLO-v5,　is　proposed　to　detect　foreign　objects　in　OPSs.　First,　the　Ghostnet-v2　is　improved　to　compress　the　lightweight　backbone　model　to　slim　down　the　original　YOLO-v5　and　reduce　the　complexity　and　computation.　Then,　a　parameter-free　attention　mechanism　SimAM　is　introduced　to　enhance　the　detection　precision　of　OPS-YOLO.　Furthermore,　the　Focal-EIoU　is　chosen　as　the　loss　function　of　the　OPS-YOLO　to　improve　object　border　localization　precision　while　accelerating　bounding　box　regression　speed.　To　evaluate　the　performance　of　OPS-YOLO,　we　generate　a　customized　dataset　of　OPS　scenarios　by　using　augmentation　methods　to　expand　the　existing　samples　and　improve　training　results.　Finally,　we　extensive　experimental　tests　on　the　dataset　are　conducted　to　verify　the　proposed　method.　Results　show　that　the　proposed　OPS-YOLO　improves　the　mAP　by　0.5%　compared　to　the　classical　YOLO-v5s　network.　Additionally,　OPS-YOLO　compresses　the　model　parameters　by　37.9%　and　reduces　the　model　size　by　37.5%.　It　is　demonstrated　that　OPSYOLO　can　achieve　a　balance　between　computational　efficiency　and　detection　accuracy,　making　it　suitable　for　automating　OPS　supervision　and　reducing　reliance　on　human　inspection.　©　2024　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.