To　timely　detect　landslide　hazards　to　start　emergency　rescue,　an　improved　Faster　R-CNN　algorithm　is　proposed　for　remote　sensing　image　landslide　detection.　First,　the　gamma　transform　and　Gaussian　filtering　methods　of　image　enhancement　are　used　to　improve　the　quality　of　the　images.　Second,　the　effect　of　batchsize　size　on　the　model　is　eliminated　using　the　group　normalization　method.　Finally,　multiscale　feature　fusion　is　performed　by　adding　a　feature　pyramid　network　structure　to　optimize　the　extracted　landslide　small　target　features,　and　then　the　backbone　network　is　set　as　deep　residual　shrinkage　network　50　to　make　the　model　more　focused　on　information　useful　for　landslide　detection.　The　experimental　results　show　that　the　improved　model　improves　the　accuracy　rate　as　well　as　the　average　precision　by　8.8%　and　8.4%,　respectively,　compared　with　the　unimproved　Faster　R-CNN,　and　compared　with　the　first-stage　models,　such　as　you　only　look　once　version　4　and　single-shot　detector,　which　verify　the　superiority　of　the　model　in　our　study　and　can　detect　landslide　targets　well.　©　2022　Society　of　Photo-Optical　Instrumentation　Engineers　(SPIE).