Grounded　on　the　existing　low-light　enhancement　algorithm,　zero-reference　deep　curve　estimation(Zero-DCE),　an　improved　Zero-DCE　low-light　enhancement　algorithm　is　proposed　to　increase　the　accuracy　of　night　fatigue　driving　detection.　Firstly,　the　upper　and　lower　sampling　structure　is　introduced　to　reduce　the　influence　of　noise.　Secondly,　the　attention　gating　mechanism　is　employed　to　improve　the　sensitivity　of　the　network　to　the　face　region　in　the　image,　and　thus　the　detection　rate　is　increased　effectively.　Then,　an　improved　kernel　selecting　module　is　proposed　for　the　problems　arising　from　noise.　Furthermore,　standard　convolution　of　Zero-DCE　is　replaced　by　the　depthwise　separable　convolution　of　MobileNet　to　accelerate　the　detection.　Finally,　the　driver　fatigue　state　can　be　judged　by　the　face　key　point　detection　network　and　classification　network.　The　experimental　results　show　that　the　proposed　algorithm　improves　the　accuracies　of　face　detection　and　eye　state　recognition　rate　in　a　night　environment　with　satisfactory　detection　results　compared　with　the　existing　fatigue　driving　detection　algorithms.　©　2022　Journal　of　Pattern　Recognition　and　Artificial　Intelligence.　All　rights　reserved.