The　detection　of　wildfire　in　transmission　line　corridors　under　multiple　background　interference　suffers　from　high　false　and　missed　detection　rates,　and　the　difficulty　of　detecting　wildfire　and　smoke　in　small　targets,　etc.　The　YOLOv5s　model　has　high　real-time　performance,　but　the　accuracy　of　detecting　wildfire　in　the　actual　power　grid　requires　to　be　improved.　Consequently,　a　flame　and　smoke　detection　method　based　on　YOLOv5s-EBWS　is　proposed　in　this　paper,　after　analyzing　the　multiple　disturbances　of　wildfire　pictures　in　transmission　line　corridors.　Firstly,　a　dataset　is　constructed　by　collecting　many　live　images　of　wildfire　incidents　along　actual　transmission　lines.　Secondly,　the　efficient　multi-scale　attention　(EMA)　mechanism　and　weighted　bi-directional　feature　pyramid　network　(BiFPN)　are　introduced　into　the　YOLOv5s　model　to　efficiently　extract　and　fully　integrate　multi-scale　spatial　information　and　important　features　of　flame　and　smoke.　Then,　wise　intersection　over　union　(WIoU)　loss　function　and　soft　non-maximum　suppression　(Soft　NMS)　method　are　introduced　to　avoid　the　missed　detection　of　overlapping　fire　and　smoke,　ultimately　to　enhance　the　generalization　of　the　model.　Finally,　example　verification　is　carried　out.　The　results　show　that　the　dataset　in　this　paper　improves　the　evaluation　indexes　of　the　model　by　12%~15%　compared　with　the　public　fire　dataset.　Compared　with　YOLOv5,　the　size　of　YOLOv5s-EBWS　model　is　reduced　by　0.4　MB,　and　the　values　of　emAP@0.5,　emAP@0.5:0.95　and　F1　for　fire　and　smoke　are　increased　by　2.3%,　3.8%,　4.9%,　and　5.5%,　respectively.　The　proposed　method　provides　a　new　way　to　detect　wildfire　occurred　in　transmission　line　corridors.　©　2025　Science　Press.　All　rights　reserved.