# US 12,111,267 B2 · System for detecting surface type of object and artificial neural network-based method for detecting surface type of object > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,111,267 B2 - Document ID: US12111267B2 - Status: Granted - Kind: B2 - Application number: 18/372,440 - Filed: 2023-09-25 - Published: 2024-01-11 - Granted: 2024-10-08 - Earliest claimed: 2019-05-15 - Assignees: Getac Holdings Corporation - Applicants: Getac Holdings Corporation - Inventors: Kun-Yu Tsai - Web page: https://patentgrep.com/patent/US12111267B2 ## CPC - G01N 21/8806: {Specially adapted optical and illumination features} - G01J 3/2823: {Imaging spectrometer} - G01N 21/01: Arrangements or apparatus for facilitating the optical investigation - G01N 21/3581: using far infrared light; using Terahertz radiation - G01N 21/8851: {Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges (G01N21/8806 and G01N21/93 - G01N21/95692 take precedence; optical measurement of dimensions G01B11/00; optical scanning G02B26/10; image transformation G06T3/00; computerised image enhancement G06T5/00; image processing per se for flaw detection G06T7/0002)} - G01N 21/9515: {Objects of complex shape, e.g. examined with use of a surface follower device (measuring contours and curvatures G01B11/24)} - G01N 21/952: Inspecting the exterior surface of cylindrical bodies or wires (G01N21/956 takes precedence) - G01N 21/956: Inspecting patterns on the surface of objects {(contactless testing of electronic circuits G01R31/308; testing currency G07D; manufacturing processes per se of semiconductor devices implementing a measuring step H10P74/20)} - G06F 17/16: Matrix or vector computation {, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization (matrix transposition G06F7/78)} - G06F 18/2148: {characterised by the process organisation or structure, e.g. boosting cascade} - G06N 3/04: Architecture, e.g. interconnection topology - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/047: Probabilistic or stochastic networks - G06N 3/063: using electronic means - G06N 3/08: Learning methods - G06N 3/09: Supervised learning - G06T 7/0004: {Industrial image inspection} - G06T 7/11: Region-based segmentation - G06T 7/40: Analysis of texture (depth or shape recovery from texture G06T7/529) - G06T 7/45: using co-occurrence matrix computation - G06T 7/586: from multiple light sources, e.g. photometric stereo - G06T 7/97: {Determining parameters from multiple pictures (depth or shape recovery from multiple images G06T7/55; stereo camera calibration G06T7/85)} - G06V 10/143: Sensing or illuminating at different wavelengths - G06V 10/145: Illumination specially adapted for pattern recognition, e.g. using gratings - G06V 10/22: by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition - G06V 10/242: {by image rotation, e.g. by 90 degrees} - G06V 10/25: Determination of region of interest [ROI] or a volume of interest [VOI] - G06V 10/60: relating to illumination properties, e.g. using a reflectance or lighting model - G06V 20/64: Three-dimensional [3D] objects - G06V 20/647: {by matching two-dimensional images to three-dimensional objects} - G01N 2021/8883: {involving the calculation of gauges, generating models} - G01N 2021/8887: {based on image processing techniques} - G06N 3/045: Combinations of networks - G06T 2207/10152: Varying illumination - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06V 2201/06: Recognition of objects for industrial automation - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) - G01J: MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY (light sources F21, H01J, H01K, H05B; investigating properties of materials by optical means G01N) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract A system for detecting a surface type of an object includes a driver component, a driver component, and a plurality of photosensitive elements. The surface of the object is divided along a first direction into a plurality of areas, and the driver component sequentially moves one of the plurality of areas to a detection position. The light source component faces the detection position and provides light of a plurality of spectra that are different from one another to illuminate the detection position. The photosensitive elements face different sections of the area at the detection position, to capture detection images of different sections of the area located at the detection position under the light of each of the spectra. One photosensitive axis of the photosensitive elements is parallel to the normal line while another photosensitive axis of the photosensitive elements is between the normal line and the first direction. ## Claims ### Claim 1 A system for detecting a surface type of an object, the system comprising: a driver component, carrying the object, wherein a surface of the object is divided along a first direction into a plurality of areas, the driver component further being for sequentially moving one of the plurality of areas to a detection position; a light source component, configured to face the detection position and provide light of a plurality of spectra that are different from one another to illuminate the detection position, wherein a light incident angle of the light of each of the spectra relative to a normal line of the area located at the detection position is more than a critical angle and less than or equal to 90 degrees, and wherein the plurality of different spectra are sequentially actuated; and a plurality of photosensitive elements, configured to face different sections of the area at the detection position and arranged along a second direction, capturing detection images of different sections of the area located at the detection position under the light of each of the spectra that are different from one another, wherein at least one photosensitive axis of the photosensitive elements is parallel to the normal line while at least another photosensitive axis of the photosensitive elements is between the normal line and the first direction. ### Claim 2 (depends on 1) The system for detecting a surface type of an object of claim 1, wherein the at least another photosensitive axis and the normal line are spaced by an included angle, and the included angle is not equal to the light incident angle. ### Claim 3 (depends on 1) The system for detecting a surface type of an object of claim 1, wherein the driver component comprises: a carrier element, carrying the object; and a step motor, connected to the carrier element, rotating the carrier element so as to drive the object to rotate. ### Claim 4 (depends on 1) The system for detecting a surface type of an object of claim 1, wherein the driver component comprises: a carrier element, carrying the object; and a step motor, connected to the carrier element, moving the carrier element along the first direction so as to drive the object to move along the first direction. ### Claim 5 (depends on 1) The system for detecting a surface type of an object of claim 1, wherein each of the photosensitive elements is a two-dimensional photosensitive element. ### Claim 6 (depends on 5) The system for detecting a surface type of an object of claim 5, further comprising: a processor, coupled to the photosensitive elements, capturing, based on a short side of each of the detection images, a middle region of each of the detection images, and combining the plurality of middle regions respectively corresponding to the plurality of areas under the same spectrum into an object image. ### Claim 7 (depends on 6) The system for detecting a surface type of an object of claim 6, wherein the processor further comprises an artificial neural network system, the artificial neural network system comprises a plurality of image matrix input channels respectively corresponding to the plurality of object images of the plurality of spectra, so as to execute a deep learning program to build a predictive model for identifying a target surface type of the object. ### Claim 8 (depends on 1) The system for detecting a surface type of an object of claim 1, wherein each of the photosensitive elements is a linear photosensitive element. ### Claim 9 (depends on 8) The system for detecting a surface type of an object of claim 8, further comprising: a processor, coupled to the photosensitive element, combining the plurality of object images under the same spectrum into an object image. ### Claim 10 (depends on 9) The system for detecting a surface type of an object of claim 9, wherein the processor further comprises an artificial neural network system for executing a deep learning program according to the plurality of object images respectively corresponding to the plurality of spectra, so as to build a predictive model for identifying a target surface type of the object. ### Claim 11 (depends on 1) The system for detecting a surface type of an object of claim 1, wherein the light source component comprises a plurality of light emitting elements, the plurality of light emitting elements respectively correspond to the plurality of spectra, and each of the light emitting elements is for emitting the light corresponding to each of the spectra. ### Claim 12 (depends on 1) The system for detecting a surface type of an object of claim 1, wherein the light source component comprises a light emitting element and a spectroscopic component, the light emitting element is for emitting multi-spectrum light, the spectroscopic component is located between the light emitting element and the detection position and comprises a plurality of filter regions respectively corresponding to the plurality of spectra, and the filter regions are for separating the multi-spectrum light into the light corresponding to the spectrum, respectively, and the spectroscopic component sequentially switches to and aligns each of the filter regions with the light emitting element. ### Claim 13 (depends on 1) The system for detecting a surface type of an object of claim 1, wherein the object is a conic object or an irregularly shaped object. ### Claim 14 (depends on 1) The system for detecting a surface type of an object of claim 1, wherein the second direction is perpendicular to the first direction.