Visible　light　positioning　(VLP)　has　become　one　of　the　most　important　technologies　for　providing　highly　accurate　location-based　services.　Despite　this,　line-of-sight　VLP　systems　have　severe　limitations　when　operating　in　complex　and　dynamic　indoor　environments.　We　propose　a　non-line-of-sight　(NLOS)　VLP　system　based　on　an　image　sensor　(IS)　and　pixel　coordinate　fingerprinting　operating　under　shadowing　and　blocking.　The　system　does　not　require　communication　between　the　transmitter　and　the　receiver　eliminating　the　need　for　a　complex　modulation　process.　For　the　first　time,　we　utilize　the　highlighted　center　pixel　coordinates　on　an　IS　as　the　fingerprint　features　and　extract　highlight　center　coordinates　using　the　deep　learning　model　Yolov8.　Furthermore,　we　propose　an　Elastic　Net　regression　with　Weighted　K-Nearest　Neighbor　Residual　Correction　algorithm　to　improve　the　positioning　performance,　which　employs　an　Elastic　Net　for　global　prediction　and　a　WKNN　algorithm　to　correct　the　global　prediction　residuals　by　leveraging　the　residual　information　from　weighted　neighboring　samples.　The　experimental　results　show　that　the　average　positioning　error　and　90th　percentile　errors　of　the　proposed　system　are　4.77　and　6.67　cm,　respectively,　with　only　16　training　points.　In　addition,　the　stability　of　the　system　is　verified　by　arbitrary　and　diagonal　sets.　　©　2014　IEEE.