5 results about "Sobel edge detection" patented technology

This invention discloses a UAV-based shield tunnel inspection system and method, applied in the field of tunnel inspection technology. By manually specifying the three-dimensional coordinate axes of the shield tunnel profile and combining this with UAV automatic navigation and computational vision, the system automates UAV tunnel inspection. The invention utilizes an onboard microcomputer to reproduce the manually set coordinate system. Simultaneously, it uses the tunnel environment and the distance from the tunnel wall to calculate the coordinates of the UAV's position within the tunnel cross-section coordinate system. Furthermore, it uses the Sobel edge detection 3D method to identify the standard lining rings of the shield tunnel, thereby calculating the axial distance, establishing a 3D model of the tunnel, and determining the UAV's travel distance within the tunnel. Finally, it uses Gaussian filtering and computational vision algorithms such as the Sobel edge detection algorithm to analyze and identify tunnel damage in the image information. This invention has advantages such as low cost, high efficiency, and high automation, and can be widely applied to tunnel inspection.

The present application relates to the technical field of visual detection, and particularly relates to an online visual detection method and system for quality of iron-on picture finished products based on industrial vision, which comprises the following steps: obtaining an initial image matrix, triggering Gaussian filter denoising through brightness variance analysis to eliminate noise interference caused by light changes; adopting a Sobel edge detection algorithm to extract pattern gradient change characteristics, generating a preliminary pattern mask based on color difference distribution analysis; combining local entropy value analysis to identify edge blur risk areas, expanding the mask boundary to contain the blurred edges, and realizing the optimization of the pattern mask; superimposing the mask to separate the main body area and verify the proportion to obtain the complete pattern area; and adopting a watershed algorithm to refine the boundary details to determine the quality result. Through the double verification mechanism of color difference distribution analysis and local entropy value analysis, the present application can adapt to complex backgrounds such as dark cloth, complex texture and color gradient, effectively improving the accuracy of pattern main body area extraction and the edge recognition precision.

The application discloses a kind of multi-modal pipeline detection data acquisition systems based on FPGA architecture, ultrasonic detection module is used to generate 8 40kHz pulse excitation signal produces ultrasonic wave, and AD9226 analog-digital converter is controlled to collect echo data, and after being internally cached, original sampling data is output through high-speed interface;Image detection module is used to collect the image data output by high-definition image sensor, executes gray scale conversion, filtering, sobel edge detection and morphological operation preprocessing, and outputs the processed image data through fiber interface, and displays on HDMI display after being cached;Two kinds of modal data are accurately time-aligned and uniformly transmitted in the system interior.This application integrates ultrasonic detection and image detection processing, transmission functions in one system, realizes the synchronous acquisition and processing of multi-dimensional information of pipeline inner wall, significantly improves the integration and data consistency of detection system, and is suitable for high-precision, low-delay acquisition and transmission of multi-modal sensing data in industrial pipeline detection equipment.

The invention discloses a point cloud image identification-based ship berthing identification anti-collision early warning monitoring method and system. The method comprises the following steps of: carrying out initial segmentation on a preprocessed image by adopting a Sobel edge detection operator; edge pixel point extraction operation is carried out on the first fine segmentation processing image, and third edge pixel points are obtained by screening pixel points which do not accord with target object identification detection conditions; based on the third edge pixel points and the second edge pixel points, ship feature angular points and a ship frame are formed through intersection point fusion, and the ship feature angular points and the ship frame are connected to obtain a third ship image; obtaining a laser point cloud image of the current ship, and secondarily verifying whether the current target object is of the same ship type or not; determining the minimum safety early warning distance of the current target object according to the current ship type; according to the method, two detection means are complementary and parallel through a visual identification processing method and three-dimensional point cloud data identification, and the accuracy of ship identification is remarkably improved.

This invention relates to the field of target detection and localization, and particularly to an image enhancement-based target detection and localization method for mobile phone assembly scenarios. This method enhances the original image acquired by a visual sensor, specifically enhancing both the input image for target detection and the input image for target edge calculation. For target detection, overall brightness enhancement, histogram equalization, and Laplace sharpening are applied to enhance brightness and contrast. The enhanced image is then input into a deep learning network for target detection. For edge detection, the input RGB image is linearly enhanced using a single R-channel, the grayscale image is enhanced using the Sigmoid function, and finally, Sobel edge detection is performed, thereby achieving the target localization process.