The　substation　plays　an　essential　role　in　the　security　and　reliability　of　the　power　supply　of　the　grid　as　it　serves　as　the　energy　conversion　hub　of　the　entire　power　system.　To　address　the　challenges　posed　by　complex　on-site　environments,　low　detection　accuracy,　and　excessive　resource　consumption　in　existing　deep　learning　models,　we　propose　a　lightweight　substation　safety　inspection　system　designed　for　front-end　devices.　The　system　consists　of　software　and　hardware　modules.　The　software　module　utilizes　weighted　bidirectional　feature　pyramid　network,　attention　mechanism,　and　pruning-quantization-distillation　operations　to　improve　and　lightweight　the　YOLOXs　(you　only　look　once　version-xs)　model,　effectively　compressing　the　model　size　while　maintaining　accuracy.　The　hardware　module　mainly　achieves　quantization　compilation　and　hardware　acceleration　of　the　lightweight　YOLOXs　detection　model　on　the　FPGA　frontend　device,　enabling　low-latency,　high-precision　real-time　detection　for　on-site　operations　at　substations.　In　Experiment,　the　improved　YOLOXs　model　shows　an　average　detection　accuracy　increase　of　2.71%　compared　to　the　original　model,　with　a　reduction　in　model　size　of　86.9%　after　light　weighting.　The　FPGA　front-end　device　achieves　a　single-image　detection　time　of　87.33　ms,　which　satisfies　the　practical　engineering　requirements　for　substation　safety　inspection.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.