6results about How to "Detailed description" patented technology

The present application relates to a kind of wide remote sensing image semantic description method and system based on adaptive region segmentation, belong to the cross-application technical field of natural language processing and computer vision.The present application first for wide remote sensing image, the semantic distribution characteristics of image are analyzed, semantic estimation point is generated, clustering is carried out to semantics, and image is segmented according to cluster center, to obtain target sub-region.For target sub-region, a small scale remote sensing image description model is used to generate the description of corresponding region, and multiple single sentence descriptions are merged according to their clustering relationship, to generate complete, clear final description.

The present application provides an asphalt pavement apparent disease evolution deduction method, system, device and medium, which belongs to the field of road engineering, transportation infrastructure operation and maintenance and intelligent detection technology, and comprises the following steps: constructing a unified space-time reference system, aligning and normalizing multi-source data in space-time; forming section-level disease quantification characteristics based on the recognition results of the inspection images and the structural indexes; event coding the maintenance measures of the road surface and pre-processing the section-level disease quantification characteristics corresponding to the time of the maintenance measures; constructing the correlation, time lag effect and spatial consistency between the disease types, disease characteristics and structural indexes to generate the collaborative evolution characteristics of the coupling relationship between diseases; constructing a deep time series prediction model to obtain the disease development trend at a specified time scale in the future, and forming the section-level disease evolution prediction result. The present application can depict the disease development law at different road sections and different time scales, and predict the future disease expansion.

The present application relates to a kind of frequency domain-space domain fusion perception methods for 2.5D integrated circuit fast high-precision thermal prediction, the network includes frequency space thermal encoder module, frequency domain cross-scale interaction module, frequency domain-space domain hybrid loss function (FSL) and model input and pre-processing network (PPNet).Frequency space thermal encoder module combines adaptive multi-frequency embedding and 3D convolutional neural network, extracts high-to-low frequency thermal dissipation gradient feature and detailed spatial feature;Frequency domain cross-scale interaction module realizes multi-scale feature interaction through cross attention mechanism, solves semantic gap problem;Frequency domain-space domain hybrid loss function loss function combines spatial mean square error and frequency domain loss, suppresses high-frequency thermal gradient noise.The present application first realizes the thermal prediction of frequency domain-space domain dual-domain perception, significantly improves prediction accuracy and speed, and has excellent generalization ability, and can be widely applied to the thermal management field of 2.5D integrated circuit.

This invention discloses a contamination judgment method, a contamination judgment system, and a cleaning method for a vehicle-mounted camera. The contamination judgment method includes the following steps: allowing a vehicle-mounted camera to capture a poster containing scene information through contaminated specimens of different gray levels to obtain a series of specimen images; converting the original image data of the series of specimen images into HSV format image data; extracting the mean and variance of the HSV color space of the HSV format image data of the series of specimen images as features and inputting them into a support vector machine algorithm model to train a classification algorithm model; inputting the image data of a detection image captured by the vehicle-mounted camera as input features into the classification algorithm model to allow the classification algorithm model to classify the detection image captured by the vehicle-mounted camera; and determining whether the lens surface of the vehicle-mounted camera is contaminated based on the classification result of the classification algorithm model.

The invention relates to a locking mechanism provided and adapted to interact with at least one locking element to establish a lock between the locking mechanism and the locking element, wherein the locking mechanism comprises a first locking pawl and a second locking pawl, which are independently rotatable from each other about a first rotation axis, wherein the first locking pawl is arranged and adapted to interact with a first locking element, and wherein the second locking pawl is arranged and adapted to interact with a second locking element, wherein a secure lock exists when the first locking element is locked to the first locking pawl and the second locking element is locked to the second locking pawl.

The invention provides an asphalt pavement apparent disease evolution deduction method, system, equipment and medium, and belongs to the technical field of road engineering, traffic infrastructure operation and maintenance and intelligent detection, and the method comprises the steps: constructing a unified space-time reference system, and carrying out the space-time alignment and normalization of multi-source data; based on an identification result of the inspection image, combining the structural indexes to form section-level disease quantitative features; carrying out event coding on the maintenance measures of the pavement and carrying out feature preprocessing on the section-level disease quantitative features at the moments corresponding to the maintenance measures; constructing correlation among disease types, disease characteristics and structural indexes, and generating co-evolution characteristics of a coupling relationship among the diseases according to a time-lag effect and space consistency; and the constructed depth time sequence prediction model obtains a disease development trend of a specified time scale in the future, and forms a section-level disease evolution prediction result. According to the method, the disease development rule can be described in different road sections and different time scales, and future disease extension is predicted.