초록 ▼

A method (30) is provided for the detection and analysis of anomalies (32) in a road surface (36). An image (34) of the road surface (36) is obtained (82) wherein traffic control markings (76) are masked (88). The image (34) is filtered (90) and a pixel map (92) is produced (98). The pixel map (92)

A method (30) is provided for the detection and analysis of anomalies (32) in a road surface (36). An image (34) of the road surface (36) is obtained (82) wherein traffic control markings (76) are masked (88). The image (34) is filtered (90) and a pixel map (92) is produced (98). The pixel map (92) is partitioned (112) into a multiplicity of subimages (108). For each subimage (108), anomaly parameters are identified (120) and a status characteristic is determined (124) and assigned (136). A subimage map (138) is produced (142) depicting the subimages (108) and their status characteristics. A determination (156) is made as to which subimages (108) contain anomalies (32). Anomaly-containing subimages (108) are grouped (158) into anomalous objects (152). For each anomalous object (152), an object type (162) is determined (160) and assigned (164). An object map (154) is then produced (166) depicting the anomalous objects (152).

대표청구항 ▼

[ What is claimed is:] [24.] A method for the detection and analysis of anomalies in a road surface, said method comprising the steps of:a) obtaining an image of said road surface including any number of said anomalies;b) masking, within said image, traffic control markings upon said road surface;c)

[ What is claimed is:] [24.] A method for the detection and analysis of anomalies in a road surface, said method comprising the steps of:a) obtaining an image of said road surface including any number of said anomalies;b) masking, within said image, traffic control markings upon said road surface;c) filtering said image to enhance a depiction of a line anomaly, which includes a crack, a seam, and an edge of said road surface, by removing non-line-anomalous features of said image, said filtering step c) producing a line pixel map;d) partitioning said line pixel map into a multiplicity of line subimages, wherein each of said line subimages includes a plurality of pixels;e) identifying, for each of said line subimages, parameters of said line anomaly;f) comparing, for each of said line subimages, said identified parameters against predetermined parameters of a hypothetical line anomaly;g) assigning a status characteristic to each of said line subimages in response to said comparing step f);h) producing a line-subimage map depicting said line subimages and said status characteristics thereof;i) filtering said image to enhance a depiction of a linear-area anomaly, which includes a sealed crack of said road surface, by removing non-linear-area-anomalous features of said image, said linear-area-anomaly filtering step i) producing a linear-area pixel map;j) filtering said image to enhance a depiction of a block-area anomaly, which includes one of a patch and a pothole of said road surface, by removing non-block-area-anomalous features of said image, said block-area-anomaly filtering step j) producing a block-area pixel map;k) combining said linear-area pixel map and said block-area pixel map to produce a combined area pixel map;l) partitioning said combined area pixel map into a multiplicity of area subimages, wherein each of said area subimages includes a plurality of pixels;m) identifying, for each of said area subimages, parameters of an area anomaly contained therein, said area anomaly being one of said linear-area anomaly and said block-area anomaly;n) comparing, for each of said area subimages, said identified parameters against predetermined parameters of a hypothetical area anomaly;o) assigning a status characteristic to each of said area subimages in response to said comparing step n);p) producing an area-subimage map depicting said area subimages and said status characteristics thereof;q) fusing said line-subimage map and said area-subimage map to produce a fused subimage map so that each of said corresponding line and area subimages forms a fused subimage;r) inspecting each of said fused subimages to determine which of said fused subimages is an anomaly-containing subimage having one of said line anomalies and said area anomalies contained therein;s) grouping said anomaly-containing subimages into an object when said anomaly-containing subimages are proximate each other;t) determining an object type, which indicates one of said crack, said seam, said edge, said sealed crack, said patch, and said pothole of said surface, for said object from spatial relationships between said anomaly-containing subimages and from said status characteristics thereof;u) assigning said object type to said anomalous object; andv) producing an object map of said road surface, said object map depicting said anomalous object and said object type.