Timely　and　effective　locust　detection　to　prevent　locust　plagues　is　crucial　for　safeguarding　agricultural　production　and　ecological　balance.　However,　under　natural　conditions,　the　＇colour　mixing　mechanism＇　of　locusts　and　the　small　scale　of　locusts　in　high-resolution　images　make　it　difficult　to　detect　locusts.　In　this　study,　we　propose　a　multi-scale　prediction　network　SSK-Yolo　based　on　YoloV5　to　effectively　solve　the　above　two　problems.　Firstly,　in　the　data　preprocessing　stage,　in　order　to　better　adapt　to　the　relatively　small-scale　targets,　we　use　the　k-means　algorithm　to　cluster　the　a　priori　frames　to　obtain　anchor　frames　with　appropriate　scale　sizes.　Secondly,　in　the　backbone,　we　still　use　the　traditional　convolution　to　extract　the　shallow　graphical　features,　and　we　use　swin-transformer　to　extract　the　deep　semantic　features,　so　as　to　improve　the　accuracy　of　feature　extraction　and　fusion　for　small　targets　in　high-resolution　images.　In　addition,　in　the　data　post-processing　stage,　we　replace　the　NMS　algorithm　with　the　soft-nms　algorithm　by　setting　a　Gaussian　function　for　the　neighbouring　detection　frames　based　on　the　overlapping　part　instead　of　suppressing　all　of　them.　A　series　of　experimental　results　on　the　publicly　available　East　Asian　locust　dataset　demonstrate　that　SSK-Yolo　outperforms　YoloV5　with　a　5%　improvement　in　precision,　1.64%　in　recall,　12%　in　mAP,　and　2.66%　in　F1-score.　SSK-Yolo　provides　an　efficient　and　viable　solution　for　locust　detection　in　the　field　of　pest　and　disease　control.　©　2024,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.