Wearing　a　safety　helmet　and　a　reflective　vest　is　essential　for　ensuring　worker　safety.　While　YOLO-based　object　detectors　have　demonstrated　significant　accuracy　in　detecting　dress　code　violations,　they　often　struggle　with　detecting　small　targets　and　maintaining　a　global　focus.　To　address　these　challenges,　we　propose　MSCG-YOLO,　a　novel　algorithm　based　on　YOLO　networks　for　worker　detection.　Our　approach　integrates　multi-head　self-attention　(MHSA)　into　the　backbone　network　and　neck　connections,　enhancing　the　model＇s　global　field　of　view　and　its　ability　to　detect　small　and　obscured　targets.　To　further　improve　small　target　detection,　we　designed　a　new　neck　structure　called　consolidative　informative　systematic　neck　(CISNeck),　which　includes　additional　layers　and　an　enhanced　detection　head.　We　also　developed　the　superficial　feature　fusion　module　(SFFM)　to　optimize　the　high-resolution　features　of　the　fourth　detection　head.　Generalized　intersection　over　union　(GIoU)　was　used　as　the　loss　function.　Experimental　results　on　custom　datasets　show　that　MSCG-YOLO　outperforms　existing　methods,　achieving　AP　and　AP50　values　of　52%　and　91.6%　on　the　validation　set,　and　53.6%　and　91%　on　the　test　set.　Compared　to　YOLOv8n,　MSCG-YOLO　improves　AP50　scores　by　3.4%　on　the　validation　set　and　2.7%　on　the　test　set.　In　conclusion,　this　study　effectively　addresses　the　practical　needs　of　dress　code　detection　in　construction　scenarios.