The　requirements　of　distance　measurement　have　increased　with　the　development　of　auto　vehicle　driving.　Traditional　methods　for　distance　estimation　require　the　complex　calibration　from　intrinsic　and　external　parameters　of　the　camera.　Recent　methods　based　on　the　neural　network　structure　mainly　measure　the　relative　depth　of　whole　images.　We　adopt　monocular　vision　with　instance　segmentation　and　camera　focal　length　to　detect　the　absolute　distance　of　front　cars　from　in-car　cameras.　First,　we　extract　the　location　of　the　cars　from　the　object　detection　network.　Second,　the　location　of　cars　is　sent　to　the　vehicle　classification　network　and　instance　segmentation　network　to　obtain　the　type　of　the　cars　and　their　mask　value.　Here,　we　use　a　model　trained　by　the　CompCars　dataset　to　classify　car　types,　and　we　train　a　new　instance　segmentation　model　using　the　Cityscapes　dataset　to　obtain　each　car＇s　mask.　Third,　in　accordance　with　the　camera　imaging　principle,　the　absolute　distance　of　cars　in　the　images　is　calculated　based　on　the　relationship　between　the　size　information　of　different　car　types　and　their　mask　values.　The　proposed　method　is　examined　with　the　KITTI　dataset,　and　the　experiment　shows　that　its　results　can　be　close　to　the　ground　truth.　Moreover,　the　proposed　method　uses　the　instance　segmentation　network　to　reduce　complexity　of　the　depth　estimation　process　and　it　can　still　generate　satisfactory　results　even　when　cars　are　partly　occluded.　©　2018　SPIE　and　IS&T.