In　industrial　production,　personal　protective　equipment　(PPE)　protects　workers　from　accidental　injuries.　However,　wearing　PPE　is　not　strictly　enforced　among　workers　due　to　all　kinds　of　reasons.　To　enhance　the　monitoring　of　workers　and　thus　avoid　safety　accidents,　it　is　essential　to　design　an　automatic　detection　method　for　PPE.　In　this　paper,　we　constructed　a　dataset　called　FZU-PPE　for　our　study,　which　contains　four　types　of　PPE　(helmet,　safety　vest,　mask,　and　gloves).　To　reduce　the　model　size　and　resource　consumption,　we　propose　a　lightweight　object　detection　method　based　on　deep　learning　for　superfast　detection　of　whether　workers　are　wearing　PPE　or　not.　We　use　two　lightweight　methods　to　optimize　the　network　structure　of　the　object　detection　algorithm　to　reduce　the　computational　effort　and　parameters　of　the　detection　model　by　32%　and　25%,　respectively,　with　minimal　accuracy　loss.　We　propose　a　channel　pruning　algorithm　based　on　the　BN　layer　scaling　factor　gamma　to　further　reduce　the　size　of　the　detection　model.　Experiments　show　that　the　automatic　detection　of　PPE　using　our　lightweight　object　detection　method　takes　only　9.5　ms　to　detect　a　single　video　frame　and　achieves　a　detection　speed　of　105　FPS.　Our　detection　model　has　a　minimum　size　of　1.82　MB　and　a　model　size　compression　rate　of　86.7%,　which　can　meet　the　strict　requirements　of　memory　occupation　and　computational　resources　for　embedded　and　mobile　devices.　Our　approach　is　a　superfast　detection　method　for　green　edge　computing.