Ensuring　correct　helmet　usage　is　vital　for　mitigating　head　injuries　in　construction　environments.　Although　YOLO-based　detectors　are　proficient　in　dress　code　recognition,　their　performance　is　often　constrained　when　detecting　small　objects.　We　introduce　ADSP-YOLO,　an　advanced　algorithm　derived　from　YOLOv8n　and　specifically　optimized　for　helmet　detection.　Central　to　this　design　is　the　adaptive　boundary-semantic　aggregation　module,　which　replaces　the　traditional　feature　pyramid　network　and　incorporates　a　specialized　detection　head　tailored　for　small　targets.　To　further　enhance　detection　efficacy,　we　propose　the　scale　sequence　feature　fusion,　leveraging　edge　information　from　the　P2　feature　layer,　and　integrate　the　dynamic　head　with　attention　modules　to　refine　accuracy.　Model　optimization　is　achieved　through　the　layer-adaptive　magnitude-based　pruning　method,　enabling　a　balance　between　compression　and　performance.　Evaluations　on　the　safety　helmet　wearing　dataset　demonstrate　that　ADSP-YOLO　achieves　a　mAP@0.5　92.7%　on　the　test　set,　surpassing　YOLOv8n　by　2.2%　and　4.2%　in　mAP@0.5　and　APs,　respectively,　while　reducing　model　parameters　to　39.3%　and　model　size　to　45.8%　of　the　original.　Moreover,　ADSP-YOLO　achieves　a　mAP@0.5　of　82.3%　on　the　GDUT-HWD　dataset　and　demonstrates　robust　adaptability　on　the　remote　sensing　object　detection　dataset,　highlighting　its　potential　for　broader　applications　in　small　object　detection.