8results about How to "Optimize detection results" patented technology

The present application belongs to the technical field of computer vision, video anomaly detection and video anomaly detection model design, and particularly relates to a video anomaly detection method, comprising: acquiring segment feature data of a video to be detected; inputting the segment feature data into a video anomaly detection model to acquire a detection result, wherein the video anomaly detection model comprises: an anomaly feature enhancement model, an anomaly score calculation model, an anomaly positioning model based on classification guidance and an anomaly score optimization model. The present application enhances the feature data of the video to be detected by the anomaly feature enhancement model in terms of time sequence information and motion information, so that it is more suitable for the anomaly detection task, and according to the anomaly positioning model based on classification guidance, parameters in the video anomaly process classification are mined and the anomaly score obtained by the anomaly score calculation model is optimized, so that a detection result with higher accuracy is obtained. Meanwhile, the anomaly positioning model based on classification guidance is convenient to use and can be installed in a memory for use as plug as needed.

The application belongs to the field of biological drugs, and discloses a quality detection method of protein polymer, which comprises at least one of reverse phase HPLC detection, SDS-PAGE analysis, mass spectrometry detection, particle size detection, exosome surface marker detection, sterility detection and endotoxin detection. The quality detection method of some examples of the application can effectively realize the separation of components in the protein polymer sample by optimizing the HPLC condition, achieve better detection results, and establish the quality control standard of the protein polymer.

The application discloses a remote sensing image target detection method and system based on an optimized SSD algorithm, first, inputting a to-be-detected remote sensing image into an efficient remote sensing image target feature extraction network to extract key features, and obtaining a required feature map; then inputting the obtained feature map into a forward and reverse iterative fusion multi-scale feature network to perform feature fusion, and generating a new feature map; finally, utilizing an anchor box matching network based on a clustering algorithm to perform anchor box clustering matching on different categories of targets in the image according to the new feature generated by fusion, so that each to-be-detected target obtains the most suitable anchor box as a target detection result; the original feature extraction network VGG16 in the SSD algorithm is optimized, an improved RFB module and an efficient attention mechanism are added into the network, the feature extraction capability of the network is improved, the anchor box matching network based on the clustering algorithm is integrated into the algorithm, the accuracy of the anchor box matching target of the algorithm is improved, and therefore the detection capability is improved.

This invention relates to a method for analyzing the behavior of stored grain pests based on YOLOv5 and an improved SORT algorithm, belonging to the technical field of stored grain pest behavior analysis methods. This method utilizes a camera to collect and filter videos of stored grain pests crawling, and processes the videos frame by frame. First, each frame is preprocessed using OpenCV to detect the outline of the stored grain pests and extract target coordinate data. When target overlap or loss occurs in a frame, resulting in detection failures for certain stored grain pests, the frame is segmented and fed into the YOLOv5 target detection network for re-detection, optimizing the detection results. This method solves the problems of low accuracy, susceptibility to environmental factors, and poor detection performance when overlap occurs in existing stored grain pest behavior analysis methods, making it particularly suitable for the needs of stored grain pest behavior analysis.

A fluorescence-magnetic resonance dual-modality contrast agent, a preparation method thereof and the use thereof. The contrast agent has the following structure: X-L-Y, wherein: formula (I); R1, R2, R3, R4, R5, R6, R7, R8, R9, R10, R11, and R12 are each independently selected from H, halogen, OH, NH2, COOH, CONH2, NO2, CN, and low alkyl groups, the low alkyl groups can be substituted with halogen, OH, NH2, COOH, CONH2, SO3H, NO2, and CN, wherein R3 and R4, taken together with the carbon atoms to which they are attached, may form a phenyl group or a heterocyclic group; R7 and R8, taken together with the carbon atoms to which they are attached, may form a phenyl group or heterocyclic group; L is a linking group; and Y is a metal chelate.

The application discloses an infrared dim small target detection method based on a deep sparse low-rank neural network, and belongs to the field of infrared data processing in remote sensing digital image processing. Due to the small volume and low brightness of the infrared dim small target, the target is difficult to be detected from an image, and therefore, the application is proposed to improve the performance of an infrared dim small target detection algorithm in small target detection and solve the problem of inaccurate target detection result in a clutter background. Specifically, the original infrared image is segmented into a series of infrared image blocks by using a sliding window; a target detection model based on target sparse representation and background low-rank constraint is established; each variable of the target detection model is solved by using an alternating direction multiplier method after the infrared image block is input; the model is expanded into a convolutional neural network, and relevant parameters in the model are continuously updated; a target detection result in the obtained reconstructed infrared image block is obtained; and the target detection result of the infrared image is output. The application can obtain good detection results for infrared targets with different attributes in different background environments.

The application belongs to the technical field of user detection, and particularly relates to an abnormal user detection method and system based on multiple feature layers, which effectively fuse user behavior patterns, user attributes and user tweet features in social data, summarize social network user data into four levels of user statistical attributes, user behavior attributes, user network attributes and high-dimensional attributes, and use a composite neural network model based on an attention mechanism to classify and predict normal users and abnormal users, so that better abnormal user detection results can be obtained.

The utility model discloses a noise testing device for an automobile exhaust tail pipe, which relates to the technical field of automobile exhaust detection and comprises a detection handle, a fixing unit is arranged on the outer surface of the detection handle, and a protective cover is arranged at one end of the detection handle. According to the device, through cooperation of the detection handle, the detection head, the adjusting block, the protective cover, the sleeve and the clamping groove, the detection head can be protected in the detection process, the influence of dust on the detection result is prevented, the protective cover is also convenient to disassemble, assemble and clean, the protective effect is maintained, and the detection efficiency is improved. And through the cooperation of the adjusting screw rod, the connecting piece, the movable ring, the second connecting rod, the first connecting rod and the supporting block, the device can be conveniently connected and fixed with the automobile tail pipe during detection, and the device is small in size, convenient and flexible to move and rapid to disassemble and assemble, so that the detection flexibility is improved, the detection time is saved, and the detection efficiency is improved.