As　a　geological　disaster　widely　distributed　in　the　southern　regions　of　China,　rainfall-induced　shallow　landslides　pose　a　significant　threat　to　affected　areas.　Timely　detection　of　landslides　is　crucial　in　the　effective　response　to　such　disasters.　However,　landslide　detection　faces　adverse　impacts　from　various　factors,　such　as　insufficient　sample　data,　complex　model　structures,　and　limitations　in　detection　accuracy　during　the　actual　detection　process.　In　this　study,　high-quality　image　samples　were　collected　from　multiple　landslide　disaster　areas　in　southern　China,　and　a　rainfall-induced　shallow　landslide　sample　database　was　constructed　in　the　region.　Based　on　this,　a　lightweight　attention-guided　YOLO　model　(LA-YOLO)　was　proposed　to　improve　the　detection　performance　of　YOLO　model　for　rainfall-induced　shallow　landslides.　First,　CG　block　is　introduced　to　enhance　the　C2f　module,　enriching　the　feature　representation　capability　through　multiscale　feature　fusion　and　reducing　the　model＇s　parameters　and　computational　complexity.　Second,　the　SimAM　attention　module　is　used　to　focus　on　the　target　regions,　improving　feature　extraction　effectiveness.　Experimental　results　show　that　the　model　parameters　of　LA-YOLO　were　reduced　by　approximately　30%,　with　precision,　recall,　and　mean　average　precision　(mAP)　on　the　landslide　sample　dataset　increasing　by　2.6%,　0.7%,　and　2.2%,　respectively.　While　ensuring　model　detection　performance,　the　model　structure　was　significantly　optimized,　achieving　both　lightweight　and　accuracy　goals,　confirming　the　model＇s　superiority　in　monitoring　rainfall-induced　shallow　landslide　disasters.