The　structural　information　detection　of　road　conditions,　which　is　adopted　for　improving　driving　comfort,　patrol　inspection,　road　maintenance,　and　accident　rescue.　In　order　to　improve　the　trustworthiness　of　road　condition　detection,　a　real-time　artificial　intelligence　road　detection　system　based　on　binocular　vision　sensors　is　investigated　in　this　article.　The　system　is　deployed　on　the　low-power　edge　computing　platform,　which　can　upload　the　processing　results　to　the　cloud　through　the　Internet-of-Things　devices.　The　authors　use　binocular　disparity　information　and　image-based　lightweight　deep　segmentation　network　to　enhance　the　detection　robustness　and　accuracy　in　the　industrial　Internet-of-Things　application　scenarios.　Considering　the　small　training　dataset,　a　special　data　labeling　regularization　and　training　strategy　have　also　been　proposed　for　training　this　network.　In　addition,　we　employ　multiframes　feature　matching　and　measurement　data　filtering　to　enhance　the　measurement　accuracy.　The　experimental　results　demonstrate　that　our　monocular-binocular　fusion　framework　is　robust　and　efficient.