For　the　problems　of　unsatisfactory　detection　accuracy　and　weak　real-time　performance　in　the　complicated　illumination　scenes　in　the　existing　deep　learning　target　detection　algorithms，an　anti-illumination　target　detection　network　model　YOLO-RLG　based　on　the　YOLO　algorithm　is　proposed.　Firstly，the　RGB　data　of　the　input　model　is　converted　into　HSV　data，and　the　S　channel　with　powerful　anti-illumination　capability　is　separated　from　the　HSV　data　and　fused　with　the　RGB　data　to　generate　RGBS　data　so　that　the　input　data　has　anti-illumination　capability.　Secondly，the　backbone　network　of　YOLOV4　is　replaced　with　Ghostnet　network，with　the　model　assignment　ratio　between　ordinary　convolution　and　cheap　convolution　modified　to　improve　the　detection　efficiency　while　ensuring　the　detection　accuracy.　Finally，the　loss　function　of　the　model　is　improved　by　replacing　CIoU　with　EIoU，which　enhances　the　target　detection　accuracy　and　algorithm　robustness.　The　experimental　results　based　on　KITTI　and　VOC　datasets　indicate　that，compared　with　the　original　network　model，the　FPS　improves　by　22.54　and　17.84　f/s，with　the　model　reduced　by　210.3　M，the　accuracy（AP）improved　by　0.83%　and　1.31%，and　the　algorithm′s　anti-illumination　performance　significantly　enhanced.　©　2023　SAE-China.　All　rights　reserved.