Efficiently　detecting　intrusions　on　a　railway　perimeter　is　crucial　for　ensuring　the　safety　of　railway　transportation.　With　the　development　of　computer　vision,　researchers　have　been　actively　exploring　methods　for　detecting　foreign　object　intrusion　via　image　recognition　technology.　This　article　reviews　the　background　and　importance　of　detecting　railway　perimeter　intrusion,　summarizes　the　limitations　of　traditional　detection　methods,　and　emphasizes　the　potential　of　improving　detection　accuracy　and　efficiency　in　image　recognition　with　deep　learning　models.　Further,　it　introduces　the　development　of　deep　learning　in　image　recognition,　focusing　on　the　principles　and　progress　of　key　technologies　such　as　convolutional　neural　networks　(CNNs)　and　vision　transformers　(ViTs).　In　addition,　the　application　status　of　semantic　segmentation　and　object　detection　algorithms　based　on　deep　learning　in　detecting　railway　perimeter　intrusion　is　explored,　including　the　classification,　principles,　and　performance　of　the　algorithms　in　practical　applications.　Finally,　it　highlights　the　primary　challenges　faced　in　railway　perimeter　intrusion　detection　and　projects　future　research　directions　to　resolve　these　challenges,　including　multisource　data　fusion,　large-scale　dataset　construction,　model　compression,　and　end-to-end　multitask　learning　networks.　These　studies　support　the　accuracy　and　real-time　detection　of　railway　perimeter　intrusion,　and　provide　technical　guarantees　for　railway　transportation　monitoring　tasks.