Aiming　at　the　problem　that　the　single-stage　YOLACT　algorithm　based　on　bounding　box　detection　lacks　the　location　and　extraction　of　the　region　of　interest,　and　the　issue　that　two　bounding　boxes　overlap　and　are　difficult　to　distinguish,　this　paper　proposes　an　anchor-free　instance　segmentation　method　based　on　the　improved　YOLACTR　algorithm.　The　mask　generation　is　decoupled　into　feature　learning　and　convolution　kernel　learning,　and　the　feature　aggregation　network　is　used　to　generate　mask　features.　By　adding　position　information　to　the　feature　map,　multi-layer　transformer　and　two-way　attention　are　used　to　obtain　dynamic　convolution　kernels.　The　experimental　results　show　that　this　method　achieves　a　mask　accuracy　(AP)　of　35.2%　on　the　MS　COCO　public　dataset.　Compared　with　the　YOLACT　algorithm,　this　method　improves　the　mask　accuracy　by　25.7%,　the　small　target　detection　accuracy　by　37.1%,　the　medium　target　detection　accuracy　by　25.8%,　and　the　large　target　detection　accuracy　by　21.9%.　Compared　with　YOLACT,　Mask　R-CNN,　SOLO,　and　other　methods,　our　algorithm　shows　significant　advantages　in　segmentation　accuracy　and　edge　detail　preservation,　especially　excelling　in　overlapping　object　segmentation　and　small　target　detection,　effectively　solving　the　problem　of　incorrect　segmentation　in　instance　boundary　overlap　regions　that　traditional　methods　face.　©　2025　Chinese　Academy　of　Sciences.　All　rights　reserved.