A method of multi-sensor data fusion includes determining a plurality of first data sets using a plurality of sensors, each of the first data sets being associated with a respective one of a plurality of sensor coordinate systems, and each of the sensor coordinate systems being defined in dependence of a respective one of a plurality of mounting positions for the sensors; transforming the first data sets into a plurality of second data sets using a transformation rule, each of the second data sets being associated with a unified coordinate system, the unified coordinate system being defined in dependence of at least one predetermined reference point; and determining at least one fused data set by fusing the second data sets.

An ensemble learning prediction method includes: establishing a plurality of base predictors based on a plurality of training data; initializing a plurality of sample weights of a plurality of sample data and initializing a processing set; in each iteration round, based on the sample data and the sample weights, establishing a plurality of predictor weighting functions of the predictors in the processing set and predicting each of the sample data by each of the predictors in the processing set for identifying a prediction result; evaluating the predictor weighting functions, and selecting a respective target predictor weighting function from the predictor weighting functions established in each iteration round and selecting a target predictor from the predictors in the processing set to update the processing set and to update the sample weights of the sample data.

Systems, methods and computer program products for detecting objects using a multi-detector are disclosed, according to various embodiments. In one aspect, a computer-implemented method includes defining an analysis profile comprising an initial number of analysis cycles dedicated to each of a plurality of detectors, where each detector is independently configured to detect objects according to a unique set of analysis parameters and/or a unique detector algorithm. The method also includes: receiving digital video data that depicts at least one object; analyzing the digital video data using some or all of the detectors in accordance with the analysis profile, where the analyzing produces an analysis result for each detector used in the analysis. Further, the method includes updating the analysis profile by adjusting the number of analysis cycles dedicated to at least one of the detectors based on the analysis results.

Disclosed is a method for detecting Ophiocephalus argus cantor under intra-class occulusion based on cross-scale layered feature fusion, including image collecting, image processing and network model, where collected images are labeled, image sizes are adjusted to obtain input images, and the input images are input into an object detection network, integrated by convolution and inserted into cross-scale layered feature fusion modules, characterized by including dividing all features input into the cross-scale layered feature fusion modules into n layers, composed of s feature mapping subsets, and fusing features of each feature mapping subset with that of other feature mapping subsets, and connecting; carrying out convolution operation, outputting training result; adjusting network parameters by a loss function to obtain parameters for a network model; inputting final output candidate boxes into a non-maximum suppression module to screen correct prediction boxes, so that prediction result is obtained.

Disclosed are techniques for lane estimation. In aspects, a method includes receiving a plurality of camera frames captured by a camera sensor of a vehicle, assigning a first subset of the plurality of camera frames to a deep learning (DL) detector and a second subset of the plurality of camera frames to a computer vision (CV) detector based on availability of the DL and CV detectors, identifying a first set of lane boundary lines in a first camera frame processed by the DL detector, identifying a second set of lane boundary lines in a second camera frame processed by the CV detector, generating first and second sets of lane models based on the first and second sets of lane boundary lines, and updating a set of previously identified lane models based on the first set of lane models and/or the second set of lane models.

The present application discloses a video type detection method, apparatus, electronic device and storage medium. A specific implementation solution is as follows: obtaining N key frames of a first video, where N is an integer greater than 1, and a type of the first video is to be detected; obtaining M confidence scores corresponding to each of the N key frames by inputting each of the N key frames into M algorithm models corresponding to the first video type respectively, where M is an integer greater than 1; determining a confidence score of the first video by a fusion strategy algorithm model according to N×M confidence scores of the N key frames; and comparing the confidence score of the first video with a confidence score threshold corresponding to a first video type, to determine whether the type of the first video is the first video type or not.

Example methods, apparatus, and articles of manufacture to classify labels based on images using artificial intelligence are disclosed. An example apparatus includes a regional proposal network to determine a first bounding box for a first region of interest in a first input image of a product; and determine a second bounding box for a second region of interest in a second input image of the product; a neural network to: generate a first classification for a first label in the first input image using the first bounding box; and generate a second classification for a second label in the second input image using the second bounding box; a comparator to determine that the first input image and the second input image correspond to a same product; and a report generator to link the first classification and the second classification to the product.

Techniques are disclosed for assisted learning with enhanced privacy. A method comprises: sending first statistical information from a first agent to a second agent in an architecture having at least two agents, wherein a first set of sample weights correspond to training the first machine learning model, wherein the first statistical information comprises the second set of sample weights determined from a first model weight; receiving, from the second agent, second statistical information comprising the second model weight and updated first set of sample weights or, from a third agent of the architecture, third statistical information comprising a third model weight and a next iteration of the first set of sample weights; and updating the first machine learning model using the second statistical information or the third statistical information.

Systems and methods for enhanced object detection for autonomous vehicles based on field of view. An example method includes obtaining an image from an image sensor of one or more image sensors positioned about a vehicle. A field of view for the image is determined, with the field of view being associated with a vanishing line. A crop portion corresponding to the field of view is generated from the image, with a remaining portion of the image being downsampled. Information associated with detected objects depicted in the image is outputted based on a convolutional neural network, with detecting objects being based on performing a forward pass through the convolutional neural network of the crop portion and the remaining portion.

Disclosed is a content-based image retrieval (CBIR) system and related methods that serve as a diagnostic aid for diagnosing whether a dermoscopic image correlates to a skin cancer type. Systems and methods according to aspects of the invention use as a reference a set of images of pathologically confirmed benign or malignant past cases from a collection of different classes that are of high similarity to the unknown new case in question, along with their diagnostic profiles. Systems and methods according to aspects of the invention predict what class of skin cancer is associated with a particular patient skin lesion, and may be employed as a diagnostic aid for general practitioners and dermatologists.