An image scanning method for a metallic surface and an image scanning system thereof are provided. The method includes sequentially moving one of a plurality of areas on a metallic surface of an object to a detection position, providing far infrared light by a light source component facing the detection position, wherein a light wavelength of the far infrared light is associated with the object, the far infrared light illuminating the detection position with a light incident angle of less than or equal to 90 degrees relative to a normal line of the area located at the detection position, and capturing a detection image of each of the areas sequentially located at the detection position by a photosensitive element according to the far infrared light, wherein the photosensitive element faces the detection position and a photosensitive axis of the photosensitive element is parallel to the normal line.

An image detection scanning method for object surface defects and an image detection scanning system thereof are provided. The method includes capturing a test image according to test light, determining whether the test image is normal, generating a coincidence signal if the test image is normal, coinciding the object with the detection position according to the coincidence signal, sequentially moving one of a plurality of areas on a surface of the object to the detection position, providing detection light facing the detection position, the detection light illuminating the detection position with a light incident angle of less than or equal to 90 degrees relative to a normal line of the area located at the detection position, and capturing a detection image of each of the areas sequentially located at the detection position according to the detection light after the object is coincided with the detection position.

An artificial neural network-based method for detecting a surface pattern of an object includes: receiving a plurality of images, wherein the plurality of images are obtained by capturing an image of an object based on light with different lighting directions and a light incident angle is less than or equal to 90 degrees; superimposing initial images of the object images; and performing deep learning by using the plurality of initial images to build a predictive model for identifying the surface pattern of the object. Accordingly, the speed of identifying a surface pattern of an object is increased, further improving the product yield of the object.

A method for regulating a position of an object includes detecting a plurality of first alignment structures of the object under rotation of the object, wherein a plurality of second alignment structures of the object sequentially face a photosensitive element during the rotation of the object, and when the plurality of first alignment structures have reached a first predetermined state, stopping the rotation of the object and performing an image capturing procedure of the object. The image capturing procedure includes: capturing a test image of the object, wherein the test image includes an image block presenting the second alignment structure currently facing the photosensitive element; detecting the position of the image block in the test image; when the image block is located in the middle of the test image, capturing a detection image of the object.

An image detection scanning method for object surface defects and an image detection scanning system thereof are provided. The method includes sequentially moving one of a plurality of areas on a surface of an object to a detection position, providing light by a light source component facing the detection position, the light illuminating the detection position with a light incident angle of less than or equal to 90 degrees relative to a normal line of the area located at the detection position, and capturing a detection image of each of the areas sequentially located at the detection position by a photosensitive element according to the light, wherein the photosensitive element faces the detection position and a photosensitive axis of the photosensitive element is parallel to the normal line.

An image detection scanning method for object surface defects and an image detection scanning system thereof are provided. The method includes capturing a test image by a photosensitive element according to test light, determining whether a setting parameter of the photosensitive element is normal by a processor according to the test image, generating a warning signal if the setting parameter is abnormal, performing a detection procedure if the setting parameter is normal, sequentially moving one of a plurality of areas on a surface of an object to the detection position in the detection procedure, providing detection light by a light source component in the detection procedure to illuminate the detection position, and capturing a detection image of each of the areas sequentially located at the detection position by the photosensitive element according to the detection light in the detection procedure.

An optical sensor module for sensing a face of a person for user identification and authentication, where a face illumination module is provided to use an array of face illumination light sources arranged in a regular array pattern to produce illumination light which may be invisible light such as infrared light and an optical diffraction element that is located to receive illumination light beams from the face illumination light sources and to transfer each illumination light beam from each face illumination light source in the array into a patterned light beam containing illumination light spots.

An artificial neural network-based method for detecting a surface type of an object includes: receiving a plurality of object images, wherein a plurality of spectra of the plurality of object images are different from one another and each of the object images has one of the spectra; transforming each object image into a matrix, wherein the matrix has a channel value that represents the spectrum of the corresponding object image; and executing a deep learning program by using the matrices to build a predictive model for identifying a target surface type of the object. Accordingly, the speed of identifying the target surface type of the object is increased, further improving the product yield of the object.

An artificial neural network-based method for selecting a surface type of an object includes receiving at least one object image, performing surface type identification on each of the at least one object image by using a first predictive model to categorize the object image to one of a first normal group and a first abnormal group, and performing surface type identification on each output image in the first normal group by using a second predictive model to categorize the output image to one of a second normal group and a second abnormal group.

An artificial neural network-based method for detecting a surface pattern of an object includes receiving a plurality of object images, dividing each object image into a plurality of image areas, designating at least one region of interest from the plurality of image areas of each of the object images, and performing deep learning with the at least one region of interest to build a predictive model for identifying a surface pattern of the object.