The present invention generally relates to assigning and reporting the cleanliness of objects and areas, and particularly relates to utilization of object and area cleanliness states as determined by the opposing processes of cleaning and dirtying detected through various methods to provide an indication of the state of cleanliness potentially utilized to alter the process of cleaning and/or dirtying to reach a desired state of cleanliness. Detection of the cleaning and dirtying operations can be performed automatically through image processing and behavior detection of still images/video indicating the activity taking place in the area of interest and time. The state of cleanliness can then be reported to interested parties as textual reports and/or augmented reality overlays on still images/video.

A learning device includes a camera configured to acquire image data by imaging a sample of a product, a physical property information acquisition unit configured to acquire physical property information of the sample, and a processing unit configured to generate a learning model. The processing unit is configured to identify a category of the sample based on rule information relating the physical property information to the category, to generate teacher data by relating the identified category to the image data, and to generate a learning model by machine learning using the teacher data. The learning model outputs the category of the sample in response to an input of the image data of the sample.

A device capable of easily defining an area other than a surface to be inspected of a workpiece. The device includes a drawing acquisition section for acquiring drawing data of the workpiece; a designation reception section for receiving specification of the surface to be inspected of the workpiece in the drawing data; and a non-inspection area calculation section for calculating, as a non-inspection area, an image area other than the surface to be inspected in an image in a view of the imaging section when the workpiece and the imaging section are positioned at an imaging position at which at least a part of the surface to be inspected as specified falls within the view of the imaging section.

In an embodiment, a method for detecting cracks in road segments is provided. The method includes: receiving raw range data for a first image by a computing device from an imaging system, wherein the first image comprises a plurality of pixels; receiving raw intensity data for the first image by the computing device from an imaging system; fusing the raw range data and raw intensity data to generate fused data for the first image by the computing device; extracting a set of features from the fused data for the first image by the computing device; providing the set of features to a trained neural network by the computing device; and generating a label for each pixel of the plurality of pixels by the trained neural network, wherein a received label for a pixel indicates whether or not the pixel is associated with a crack.

A method for inspecting a marine vessel underdeck utilizes a video camera such as a digital video camera with a magnifying or telephoto lens. The method produces a magnified image on a monitor for viewing by an inspector that appears to be no more than about 24 inches (61 cm) away. The method includes the step of filming the underdeck of a distance of about 40-70 feet (12-21 m). The lens provides a focal length of between about 15 feet (4.6 m) and 150 feet (46 m). Thus the method is conducted at a workable focal range of between about 15 feet (4.6 m) and 150 feet (46 m). The lens preferably has a focal length of between 30 feet (9 m) and 75 feet (23m). The method includes the step of scanning the suspect area of the underdeck of a speed of about 1 inch (2.54 cm) per second to three feet (91.4 cm) per second. The preferred method contemplates scanning of the suspect area of a rate of between about 0.5-1 foot (15.2-30.5 cm) per second. The digital video can be focused on a particular area for about 15-30 seconds to create a loop for vetting.

A deterioration estimation system according to an aspect of the present disclosure includes: a memory configured to store instructions; and at least one processor configured to execute the instructions to: detect road surface deterioration from a first road surface image; determine, in the first road surface image, a first region where deterioration is detectable and a second region where deterioration is difficult to detect; calculate a degree of deterioration of the first region based on a detection result from the first road surface image; estimate a degree of deterioration of the second region based on a degree of deterioration in road surface deterioration detected from a second road surface image imaged before the first road surface image; and calculate a degree of deterioration of the road surface based on the calculated degree of deterioration of the first region and the estimated degree of deterioration of the second region.

An automatic detection method for defects of a display panel is disclosed, which comprises: acquiring a tag image, a mapped original image and a mapped tag image; dividing the mapped original image into a plurality of mapped original sub-images, and dividing the mapped tag image into a plurality of mapped tag sub-images; acquiring a normal area and a defective area of the mapped original sub-images; merging the mapped original sub-images to discriminate the normal area and the defective area of the mapped original sub-images; correcting the discriminated normal area and the discriminated defective area of the mapped original sub-images by using the mapped tag image and the tag image to acquire a defect location of the display panel. The automatic detection method for defects of the display panel can accurately acquire the location of the defect and the difference between the defective area and the normal area to quantify and discriminate the defects of the display panel.

The present disclosure discloses a method and a device for monitoring life of automobile oil. The method comprising, receiving, by an oil life indication device, a video from an imaging unit communicatively coupled to the oil life indication device, where the video displays spreading of the automobile oil over a slope surface. The method comprises extracting a plurality of image frames from the video, determining one or more parameter values for at least one quality factors of the automobile oil and comparing the one or more parameter values with predefined threshold values to estimate life of the automobile oil.

A method for inspecting a marine vessel underdeck utilizes a video camera such as a digital video camera with a magnifying or telephoto lens. The method produces a magnified image on a monitor for viewing by an inspector that appears to be no more than about 24 inches (61 cm) away. The method includes the step of filming the uuderdeck of a distance of about 40-70 feet (12-21 m). The lens provides a focal length of between about 15 feet (4.6 m) and 150 feet (46 m). Thus the method is conducted at a workable focal range of between about 15 feet (4.6 m) and 150 feet (46 m). The lens preferably has a focal length of between 30 feet (9 m) and 75 feet (23 m). The method includes the step of scanning the suspect area of the underdeck of a speed of about 1 inch (2.54 cm) per second to three feet (91.4 cm) per second. The preferred method contemplates scanning of the suspect area of a rate of between about 0.5-1 foot (15.2-30.5 cm) per second. The digital video can be focused on a particular area for about 15-30 seconds to create a loop for vetting.

A system for taking high-resolution photographs from a vehicle-mounted camera, forming orthomosaics from video and/or multiple high-resolution photographs, and using artificial intelligence to detect and classify pavement flaws and defects in the imagery. Detection also includes the ability to capture quantifiable metrics for the defects and/or a region of interest. Three-dimensional imagery is produced from the same images as the orthomosaics. Surface and terrain map products made from the same source images capture additional details such as depth and volume. The highlighted orthomosaics and three-dimensional imagery can then be used as a basis to determine the pavement surface condition and subsequently support maintenance orders and manage pavement repairs. Further, metadata such as latitude, longitude, and altitude geo-location coordinates and sampling time can also be transferred to the output products to create a digital time history and enable analysis for preventative maintenance planning. Alternatively-sourced imagery may also be analyzed.