A vehicle information detection method, an electronic device and a storage medium are provided, and relates to the technical field of artificial intelligence, in particular to the technical field of computer vision and deep learning. The method includes: determining a bird's-eye view of a target vehicle based on an image of the target vehicle; performing feature extraction on the image of the target vehicle and the bird's-eye view respectively, to obtain first feature information corresponding to the image of the target vehicle and second feature information corresponding to the bird's-eye view of the target vehicle; and determining three-dimensional information of the target vehicle based on the first feature information and the second feature information. According to embodiments of the disclosure, accurate detection of vehicle information can be realized based on a monocular image.

A system is disclosed that includes a video camera, a local image processing computer, a remote event detection server, and a remote data integration database. This technology utilizes deep learning and domain-specific customer personas to rapidly derive economically useful insights from real-time video imagery with an emphasis on personal fashion and lifestyle.

This document describes apparatuses and techniques for radar-enabled sensor fusion. In some aspects, a radar field is provided and reflection signals that correspond to a target in the radar field are received. The reflection signals are transformed to provide radar data, from which a radar feature indicating a physical characteristic of the target is extracted. Based on the radar features, a sensor is activated to provide supplemental sensor data associated with the physical characteristic. The radar feature is then augmented with the supplemental sensor data to enhance the radar feature, such as by increasing an accuracy or resolution of the radar feature. By so doing, performance of sensor-based applications, which rely on the enhanced radar features, can be improved.

A Video Semantic Segmentation System (VSSS) is disclosed that performs accurate and fast semantic segmentation of videos using a set of temporally distributed neural networks. The VSSS receives as input a video signal comprising a contiguous sequence of temporally-related video frames. The VSSS extracts features from the video frames in the contiguous sequence and based upon the extracted features, selects, from a set of labels, a label to be associated with each pixel of each video frame in the video signal. In certain embodiments, a set of multiple neural networks are used to extract the features to be used for video segmentation and the extraction of features is distributed among the multiple neural networks in the set. A strong feature representation representing the entirety of the features is produced for each video frame in the sequence of video frames by aggregating the output features extracted by the multiple neural networks.

The present disclosure provides a method for detecting a face synthetic image, an electronic device and a storage medium. The technical solution includes inputting a face image to be detected into a pre-trained convolution neural network to obtain a raw image feature of the face image; inputting the raw image feature into a first full connected layer and a second full connected layer respectively to obtain a first feature vector corresponding to a face key point of the face image and a second feature vector corresponding to the face image; merging the first feature vector and the second feature vector to obtain a merged feature vector; inputting the merged feature vector to a third full connected layer to obtain a detection result of the face image.

This document describes apparatuses and techniques for radar-enabled sensor fusion. In some aspects, a radar field is provided and reflection signals that correspond to a target in the radar field are received. The reflection signals are transformed to provide radar data, from which a radar feature indicating a physical characteristic of the target is extracted. Based on the radar features, a sensor is activated to provide supplemental sensor data associated with the physical characteristic. The radar feature is then augmented with the supplemental sensor data to enhance the radar feature, such as by increasing an accuracy or resolution of the radar feature. By so doing, performance of sensor-based applications, which rely on the enhanced radar features, can be improved.

The present invention discloses a roadside image pedestrian segmentation method based on a variable-scale multi-feature fusion convolutional network. For scenes where the pedestrian scale changes significantly in the intelligent roadside terminal image, this method designs two parallel convolutional neural networks to extract the local and global features of pedestrians at different scales in the image, and then fuses the local features and global features extracted by the first network with the local features and global features extracted by the second network at the same level, and then fuse the fused local features and global features for the second time to obtain a variable-scale multi-feature fusion convolutional neural network, and then train the network and input roadside pedestrian images to realize pedestrian segmentation. The present invention effectively solves the problems that most current pedestrian segmentation methods based on a single network structure are prone to segmentation boundary fuzziness and missing segmentation.

The disclosure relates to a method for training a neural network classifier (100) to classify a digital image depicting one or more objects of a biological sample into a specific class, which class belongs to a predefined set of classes (C1-C3), the method comprising: providing a training set of digital images (110a-s) originating 5 from a plurality of biological samples, each digital image (110a) of the training set being labeled with a specific class (C1) of the one or more objects of the digital image (110a), each digital image (110a) of the training set being associated with global data (114a) pertaining to the respective sample, training the neural network (100) using pixel data of each digital image (110a) from the training set of digital 10 images (110a-s) and the global data (114a) associated with said digital image (110a) as input, and using the specific class (C1) of the label of said digital image (110a) as a correct output from the neural network (100). The disclosure further relates to an analyzing apparatus (400).

The current invention describes systems, methods and apparatus for the combination of high-throughput flow imaging microscopy coupled with convolutional neural networks to analyze particles, such as aggregated biomolecules, and cells for use in in a variety of diagnostic, therapeutic and industrial applications.

The discloser relates to a method for evaluating an optical appearance in the surroundings of a vehicle and to a vehicle. The method has the steps of providing a captured image of the surroundings of a vehicle and extracting features from the captured image. Furthermore, the method comprises carrying out a first analysis of the captured image, wherein one or more objects are detected as surfaces and the result of the analysis is provided as a first analysis result. A second analysis of the captured image is also carried out, wherein edges of one or more objects are detected and the result of the analysis is provided as a second analysis result, the first analysis and the second analysis being carried out independently of each other. The method further comprises combining the first analysis result and the second analysis result to form an output image.