Rail　surface　defects　typically　serve　as　early　indicators　of　railway　malfunctions,　which　may　compromise　the　quality　and　corrosion　resistance　of　rails,　thereby　endangering　the　safe　operation　of　trains.　The　timely　detection　of　defects　is　essential　to　ensure　the　safe　operation　of　railways.　To　improve　the　classification　accuracy　of　rail　surface　defect　detection,　this　paper　proposes　a　rail　surface　defects　detection　algorithm　based　on　MobileNet-YOLOv7.　By　integrating　lightweight　deep　learning　algorithms　into　the　engineering　application　of　rail　surface　defect　detection,　a　MobileNetV3　lightweight　network　is　used　as　the　backbone　network　for　YOLOv7　to　enhance　both　speed　and　accuracy　in　complex　defect　extraction.　Subsequently,　the　efficient　intersection　over　union　(EIOU)　loss　function　is　utilized　as　the　positional　loss　function　to　bolster　system　resilience.　Finally,　the　k-means++　clustering　algorithm　is　applied　to　obtain　new　anchor　boxes.　The　experimental　results　demonstrate　the　effectiveness　of　the　proposed　method,　achieving　superior　detection　accuracy　compared　with　traditional　algorithms.