A framework for safely operating autonomous machinery, such as vehicles and other heavy equipment, in an in-field or off-road environment, includes detecting, identifying, classifying and tracking objects and/or terrain characteristics from on-board sensors that capture images in front and around the autonomous machinery as it performs agricultural or other activities. The framework generates commands for navigational control of the autonomous machinery in response to perceived objects and terrain impacting safe operation. The framework processes image data and range data in multiple fields of view around the autonomous equipment to discern objects and terrain, and applies artificial intelligence techniques in one or more neural networks to accurately interpret this data for enabling such safe operation.

A health condition of a person may be assessed from a thermal sensor signal. By increasing performance indices of a thermal camera (for example, resolution, frame rate, sensitivity), operation may be extended to identification verification, biometric data extraction and health condition analysis, and so forth. Prediction may be carried out by monitoring a time sequence of thermal images, and consequently early warning of the health condition may be provided. The apparatus may be used for, but not limited to, personalization of smart home devices through supervised and reinforcement learnings. The application of the apparatus may be, but not limited to, smart homes, smart buildings and smart vehicles, and so forth.

A health condition of a person may be assessed from a thermal sensor signal. By increasing performance indices of a thermal camera (for example, resolution, frame rate, sensitivity), operation may be extended to identification verification, biometric data extraction and health condition analysis, and so forth. Prediction may be carried out by monitoring a time sequence of thermal images, and consequently early warning of the health condition may be provided. The apparatus may be used for, but not limited to, personalization of smart home devices through supervised and reinforcement learnings. The application of the apparatus may be, but not limited to, smart homes, smart buildings and smart vehicles, and so forth.

The present invention belongs to the field of computer visual detection, and relates to a binocular automatic gear pitting detection device based on deep learning, comprising a gearbox system, a data acquisition system, an image processing system, a tooth surface positioning system, a control system and a motor, wherein the gearbox is used for installing paired meshing gears; the data acquisition system is arranged on the side wall of the gearbox, and a CCD industrial camera is arranged on the data acquisition system; the image processing system completes quantitative evaluation of gear pitting and target detection based on a deep learning technology; both ends of the tooth surface positioning system are respectively connected with the motor and the gearbox, and the torque of the motor is transmitted to an input shaft of the gearbox. The device can determine the optimal installation base points of the data acquisition system according to the characteristics of the meshing gears, and find effective detection areas in combination with the light source and camera arrangement solutions, thereby effectively saving the installation space of the detection device and adapting to the operating characteristics of the meshing gears.

The present invention belongs to the field of computer visual detection, and relates to a binocular automatic gear pitting detection device based on deep learning, comprising a gearbox system, a data acquisition system, an image processing system, a tooth surface positioning system, a control system and a motor, wherein the gearbox is used for installing paired meshing gears; the data acquisition system is arranged on the side wall of the gearbox, and a CCD industrial camera is arranged on the data acquisition system; the image processing system completes quantitative evaluation of gear pitting and target detection based on a deep learning technology; both ends of the tooth surface positioning system are respectively connected with the motor and the gearbox, and the torque of the motor is transmitted to an input shaft of the gearbox. The device can determine the optimal installation base points of the data acquisition system according to the characteristics of the meshing gears, and find effective detection areas in combination with the light source and camera arrangement solutions, thereby effectively saving the installation space of the detection device and adapting to the operating characteristics of the meshing gears.

Systems and methods for ossification center detection (OCD) and bone age assessment (BAA) may be provided. The method may include obtaining a bone age image of a subject. The method may include generating a normalized bone age image by preprocessing the bone age image. The method may include determining, based on the normalized bone age image, positions of a plurality of ossification centers using an ossification center localization (OCL) model. The method may include estimating, based on the normalized bone age image and information related to the positions of the plurality of ossification centers, a bone age of the subject using a bone age assessment (BAA) model.

Systems and methods for ossification center detection (OCD) and bone age assessment (BAA) may be provided. The method may include obtaining a bone age image of a subject. The method may include generating a normalized bone age image by preprocessing the bone age image. The method may include determining, based on the normalized bone age image, positions of a plurality of ossification centers using an ossification center localization (OCL) model. The method may include estimating, based on the normalized bone age image and information related to the positions of the plurality of ossification centers, a bone age of the subject using a bone age assessment (BAA) model.

The present invention discloses a method for fully automatically detecting chessboard corner points, and belongs to the field of image processing and computer vision. Full automatic detection of chessboard corner points is completed by setting one or a plurality of marks with colors or certain shapes on a chessboard to mark an initial position, shooting an image and conducting corresponding processing, using a homography matrix H calculated by initial pixel coordinates of a unit grid in a pixel coordinate system and manually set world coordinates in a world coordinate system to expand outwards, and finally spreading to the whole chessboard region. The method has the advantages of simple procedure and easy implementation; the principle of expanding outwards by a homography matrix is used, so that the running speed of the algorithm is fast; and the corner points obtained by a robustness enhancement algorithm is more accurate, so that the situation of inaccurate corner point detection in the condition of complex illumination is avoided.

A display module includes: a liquid crystal module, a cover plate, and a texture recognition unit. The texture recognition unit includes a first light source and a texture sensing module. The first light source is located at a side of the cover plate proximate to the liquid crystal module, and is configured to emit invisible light. The texture sensing module is located at a side of the liquid crystal module facing away from the cover plate. A light wavelength range of light allowed to pass through the cover plate and the liquid crystal module includes a light wavelength range of the invisible light. The texture sensing module is configured to collect reflected light after the invisible light is irradiated to a target object, so as to identify a texture of the target object.

A display module includes: a liquid crystal module, a cover plate, and a texture recognition unit. The texture recognition unit includes a first light source and a texture sensing module. The first light source is located at a side of the cover plate proximate to the liquid crystal module, and is configured to emit invisible light. The texture sensing module is located at a side of the liquid crystal module facing away from the cover plate. A light wavelength range of light allowed to pass through the cover plate and the liquid crystal module includes a light wavelength range of the invisible light. The texture sensing module is configured to collect reflected light after the invisible light is irradiated to a target object, so as to identify a texture of the target object.