The　quality　of　monitoring　data　sources　has　an　important　impact　on　road　recognition　and　human　vehicle　detection　performance　in　the　field　of　smart　transportation.　Aiming　at　the　problem　of　large　light　disturbance　and　low　contrast　in　urban　road　monitoring　images　in　practical　applications,　this　paper　proposed　a　method　for　low-light　image　enhancement　of　urban　roads　based　on　Zero-DCE.　First,　for　the　constructed　urban　road　low-light　image　data　set,　an　improved　low-light　enhancement　network　model　was　designed　based　on　the　idea　of　no-reference　depth　curve　estimation;　then,　the　model　hyperparameters　are　ablated,　and　end-to-end　training　can　be　achieved　without　relying　on　paired　training　data,　which　overcomes　the　shortcomings　of　existing　image　enhancement　algorithms　that　rely　too　much　on　high　and　low　quality　paired　images　in　the　same　scene.　The　experimental　results　show　that　the　method　proposed　in　this　paper　can　significantly　enhance　the　brightness　and　contrast　of　the　image,　improve　the　visibility　of　details,　and　improve　its　subjective　visual　quality.　At　the　same　time,　in　terms　of　objective　evaluation　indicators　and　running　time,　this　method　also　has　certain　advantages　compared　with　traditional　methods　and　other　learning-based　methods.　©　2020　ACM.