Abstract: Systems and methods for detecting objects in a video are provided. A method can include inputting a video comprising a plurality of frames into an interleaved object detection model comprising a plurality of feature extractor networks and a shared memory layer. For each of one or more frames, the operations can include selecting one of the plurality of feature extractor networks to analyze the one or more frames, analyzing the one or more frames by the selected feature extractor network to determine one or more features of the one or more frames, determining an updated set of features based at least in part on the one or more features and one or more previously extracted features extracted from a previous frame stored in the shared memory layer, and detecting an object in the one or more frames based at least in part on the updated set of features.

Abstract: A method for intelligently measuring vehicle speed based on a binocular stereo vision system includes: training a Single Shot Multibox Detector neural network to obtain a license plate recognition model; calibrating the binocular stereo vision system to acquire parameters of two cameras; detecting the license plates in the captured video frames with the license plate recognition model, locating the license plate position; performing feature point extraction and stereo matching by a feature-based matching algorithm; screening and eliminating the matching point pairs, and reserving the coordinates of the matching point pair closest to the license plate center; performing stereo measurement on the screened matching point pair to get the spatial coordinates of the position; calculating and obtaining the speed of the target vehicle.

Abstract: The invention provides a method for intelligently measuring vehicle trajectory based on a binocular stereo vision system, including the following steps: inputting a dataset into an SSD (Single Shot Multibox Detector) neural network to train a license plate recognition model; calibrating the binocular stereo vision system, and recording videos of moving target vehicles; detecting the license plates in the video frames with the license plate recognition model; performing stereo matching on the license plates in the subsequent frames of the same camera and in the corresponding left-view and right-view video frames by a feature-based matching algorithm; reserving correct matching point pairs after filtering with a homography matrix; screening the reserved matching point pairs, and reserving the one closest to the license plate center as the position of the target vehicle in the current frame; performing stereo measurement on the screened and reserved matching point pairs to get the spatial position coordinates of

Abstract: Provided is a method for preparing isavuconazonium sulfate. Specifically, the preparation method involved comprises: reacting a compound of formula V in the presence of a provided compound having a bisulfate ion so as to obtain isavuconazonium sulfate as shown in formula VI. The preparation method has the advantages of stable intermediate, easy separation and purification, simple operation, high reaction yield, and easy industrial production.

Abstract: A method for intelligently measuring vehicle speed based on a binocular stereo vision system includes: training a Single Shot Multibox Detector neural network to obtain a license plate recognition model; calibrating the binocular stereo vision system to acquire parameters of two cameras; detecting the license plates in the captured video frames with the license plate recognition model, locating the license plate position; performing feature point extraction and stereo matching by a feature-based matching algorithm; screening and eliminating the matching point pairs, and reserving the coordinates of the matching point pair closest to the license plate center; performing stereo measurement on the screened matching point pair to get the spatial coordinates of the position; calculating and obtaining the speed of the target vehicle.

Abstract: The invention provides a method for intelligently measuring vehicle trajectory based on a binocular stereo vision system, including the following steps: inputting a dataset into an SSD (Single Shot Multibox Detector) neural network to train a license plate recognition model; calibrating the binocular stereo vision system, and recording videos of moving target vehicles; detecting the license plates in the video frames with the license plate recognition model; performing stereo matching on the license plates in the subsequent frames of the same camera and in the corresponding left-view and right-view video frames by a feature-based matching algorithm; reserving correct matching point pairs after filtering with a homography matrix; screening the reserved matching point pairs, and reserving the one closest to the license plate center as the position of the target vehicle in the current frame; performing stereo measurement on the screened and reserved matching point pairs to get the spatial position coordinates of

Abstract: An encapsulated assembly of electronic componentry, suitable for incorporation into a textile or a yarn, and the assembly comprising two flexible substrates (3, 4) which encapsulate the electronic componentry, at least one of the flexible substrates comprising at least one preformed relief region (3a), which provides a volume which at least in part accommodates the electronic componentry, and the componentry located substantially at a neutral axis (N) of the assembly.