Aiming　to　address　the　issues　of　complex　detection　processes，numerous　parameters，low　accuracy，　and　slow　execution　speed　in　current　driver　fatigue　detection　algorithms，we　propose　a　lightweight　model　based　on　an　improved　YOLOv8n-Pose.　This　model　optimizes　the　structure　of　YOLOv8n-Pose.　Firstly，　Ghost　convolution　is　introduced　into　the　backbone　network　to　reduce　the　number　of　model　parameters　and　unnecessary　convolution　computations.　Secondly，a　Slim-neck　is　introduced　to　fuse　features　of　different　sizes　extracted　by　the　backbone　network，accelerating　network　prediction　calculations.　Additionally，an　occlusion-aware　attention　module　（SEAM）　is　added　to　the　neck　part　to　emphasize　the　facial　region　in　images　and　weaken　the　background，improving　keypoint　localization　accuracy.　Finally，a　GNSC-Head　structure　is　proposed　in　the　detection　head　part，which　incorporates　shared　convolution　and　optimizes　the　BN　layers　of　traditional　convolution　with　more　stable　GN　layers，effectively　saving　model　parameter　space　and　computational　resources.　Experimental　results　show　that　compared　with　the　original　algorithm，the　improved　YOLOv8n-Pose　increases　mAP@0.　5　by　0.　9%，reduces　parameter　count　and　computational　cost　by　50%，and　increases　FPS　by　8%.　The　final　fatigue　driving　recognition　rate　reaches　93.　5%.　Verified　through　experiments，this　algorithm　maintains　high　detection　accuracy　while　being　lightweight　and　effectively　recognizes　driver　status，providing　strong　support　for　deployment　on　vehicle　edge　devices.　©　2025,　Science　Press.　All　rights　reserved.