A method is described for determining a confidence value for an object of a class determined by a neural network in an input image. The method includes: preparing an activation signature with the aid of a multiplicity of output images of a layer of the neural network for the class of the object, with the input image being provided to the input of the neural network; scaling the activation signature to the size of the input image; comparing an overlapping area portion of an area of the activation signature with an area of an object frame in relation to the area of the activation signature in order to determine the confidence value.

A method for automatically panning a view for a commercial vehicle includes determining a plurality of estimated trailer angles. Each estimated trailer angle is determined using a distinct estimation method, and method assigns a confidence value to each estimated trailer angle in the plurality of estimated trailer angles. The method determines a weighted sum of the plurality of estimate trailer angles, and automatically pans the view based at least in part on the weighted sum and a current vehicle operation.

Sensor fusion is performed for efficient deep learning processing. A camera image is received from an image sensor and supplemental sensor data is received from one or more supplemental sensors, the camera image and the supplemental sensor data including imaging of a cabin of a vehicle. Regions of interest in the camera image are determined based on one or more of the camera image or the supplemental sensor data, the regions of interest including areas of the camera image flagged for further image analysis. A machine-learning model is utilized to perform object detection on the regions of interest of the camera image to identify one or more objects in the camera image. The objects are placed into seating zones of the vehicle.

A manufacturing data analyzing method and a manufacturing data analyzing device are provided. The manufacturing data analyzing method includes the following steps. Each of at least one numerical data, at least one image data and at least one text data is transformed into a vector. The vectors are gathered to obtain a combined vector. The combined vector is inputted into an inference model to obtain a defect cause and a modify suggestion.

An advanced driver assist system (ADAS) includes a processing circuit and a memory storing instructions executable by the processing circuit. The processing circuit executes the instructions to cause the ADAS to: obtain, from a vehicle, a video sequence including a plurality of frames captured while driving the vehicle, where each of the frames corresponds to a stereo image including a first viewpoint image and a second viewpoint image; determine depth information in the stereo image based on reflected signals received while driving the vehicle; fuse the stereo image and the depth information to generated fused information, and detect at least one object included in the stereo image based on the fused information.

One or more multi-stage optimization iterations are performed with respect to a compression algorithm. A given iteration comprises a first stage in which hyper-parameters of a perceptual quality algorithm are tuned independently of the compression algorithm. A second stage of the iteration comprises tuning hyper-parameters of the compression algorithm using a set of perceptual quality scores generated by the tuned perceptual quality algorithm. The final stage of the iteration comprises performing a compression quality evaluation test on the tuned compression algorithm.

This application provides an image obtaining method and apparatus. The image obtaining method according to this application includes: obtaining first original image data, where the first original image data is captured by an image sensor based on an initial visible light exposure parameter and luminous intensity of an infrared illuminator; obtaining a luminance of a visible light image based on the first original image data; adjusting the visible light exposure parameter based on a first difference, where the first difference is a difference between the luminance of the visible light image and preset target luminance of the visible light image; obtaining a luminance of an infrared image based on the first original image data; adjusting the luminous intensity of the infrared illuminator based on a second difference, where the second difference is a difference between the luminance of the infrared image and preset target luminance of the infrared image.

Provided herein are systems and methods for estimating contact pressure of a human lying on a surface including one or more imaging devices having imaging sensors oriented toward the surface, a processor and memory, including a trained model for estimating human contact pressure trained with a dataset including a plurality of human lying poses including images generated from at least one of a plurality of imaging modalities including at least one of a red-green-blue modality, a long wavelength infrared modality, a depth modality, or a pressure map modality, wherein the processor can receive one or more images from the imaging devices of the human lying on the surface and a source of one or more physical parameters of the human to determine a pressure map of the human based on the one or more images and the one or more physical parameters.

A method includes receiving, from a sensing system of an autonomous vehicle (AV), image data including first image data and second image data. The method further includes determining, for a frame, whether an amount of image data matching between the first image data and the second image data satisfies a first threshold condition, in response to determining that the amount of image data matching satisfies a first threshold condition, identifying the frame as invalid, determining whether a number of consecutive frames determined to be invalid satisfies a second threshold condition, and in response to determining that the number of consecutive frames determined to be invalid satisfies the second threshold condition, generating a notification that the sensing system is outputting invalid data.

Techniques are disclosed for systems and methods for water non-water segmentation of navigational imagery to assist in the autonomous navigation of mobile structures. An imagery based navigation system includes a logic device configured to communicate with an imaging module coupled to a mobile structure and/or configured to capture images of an environment about the mobile structure. The logic device may be configured to receive at least one image from the imaging module; determine a water/non-water segmented image based, at least in part, on the received at least one image, and generate a range chart corresponding to the environment about the mobile structure based, at least in part, on the determined water/non-water segmented image and/or the received at least one image.