41results about How to "Precise target position" patented technology

The present invention relates to a deep learning-based UAV intelligent inspection cable surface defect detection method, which uses the YOLOv3 surface defect detection model to perform training and detection respectively, and uses the UAV to collect and verify cable image data and apply the constructed defect detection model , Evaluate and analyze application errors, construct a reasonable and effective evaluation mechanism, the invention can improve the identification accuracy of defects in cables, have a better ability to identify defects in cables at the initial stage, and can detect cable defects in time and quickly deal with them.

The invention discloses a forward-looking radar imaging sea-surface target key point detection and recognition method. The method includes the steps that radar echo data are quantized into a gray scale image; a region of interest is extracted from the gray scale image, partition is performed, and a target region partition image is obtained; the radar echo data and the region of interest of the image are used, and a target region peak point information matrix is obtained; information merging is conducted on the target region partition image and the target region peak point information matrix, the number K of peak points in a target region in a merging result is obtained through counting, the previous K peak points are selected and serve as target effective peak points and form a target effective peak point image in a binaryzation mode, target axial features are extracted, and a target position is determined; the center of gravity of target energy is calculated and serves as the target key point. According to forward-looking radar target features, multiple mode identification methods are used comprehensively, the inherent features of a target can be reserved, meanwhile interference factors such as artifacts and side lobes can be restrained, and the recognition accuracy and the positioning precision of radar imaging sea surface target key points are improved.

The invention provides a positioning method and device, a robot, a storage medium and a positioning system. An identification code is arranged at a preset position point of the target area; the identification code is generated according to the position information of the position point in a preset map of the target area; the position information of the position point is matched with the scale granularity of the map; the identification code acquired by the mobile device is the accurate position information in the target area, and meanwhile, the depth image acquired by the mobile device comprises the imaging of the position point, so that the accurate position of the mobile device in the map of the target area can be obtained by combining the identification code and the depth image. Besides,the target position is determined by receiving the signal transmitted by the signal transmitting device preset at the target position, so that the target position is accurate, and the mobile device can reach the accurate target position according to the navigation route from the initial position to the target position.

The invention provides a liquid crystal panel delamination detection device and method, the device comprising: a base, a mobile platform, a natural light source camera assembly, a first ultraviolet light source camera assembly, a second ultraviolet light source camera assembly, an upper light source assembly and a backlight Assemblies, the mobile carrier adopts an air-floating platform, and the mobile carrier is set above the base. The natural light source camera assembly, the upper light source assembly and the first ultraviolet light source camera assembly are all located on one side of the mobile carrier. The first ultraviolet light source camera assembly and the second The two ultraviolet light source camera components are symmetrically arranged with the liquid crystal panel as the axis, and the second ultraviolet light source camera component and the backlight source component are located on the other side of the liquid crystal panel; wherein, the upper light source component includes a first natural light source component and a first ultraviolet light source component, The backlight source assembly includes a second natural light source assembly and a second ultraviolet light source assembly. The invention can automatically distinguish the defects on the surface of the liquid crystal panel and the defects inside the liquid crystal panel, thereby improving the detection efficiency.

The invention discloses a crossroad non-motor vehicle and pedestrian position detection method based on special-purpose short-distance communication. The method comprises the following steps: disposing a road side special-purpose short-distance communication unit and a roadbed computer unit at a crossroad turning area, wherein the road side special-purpose short-distance communication unit and the roadbed computer unit are connected; the road side special-purpose short-distance communication unit obtaining crossroad area wireless signal data frames; and by taking the data frames as input signals, performing real-time signal spectrum analysis, window function weighing and multipath signal inhibition processing through the roadbed computer unit such that time sequence based non-motor vehicle and pedestrian position detection in the crossroad turning area is realized. The method provided by the invention has the advantages of being free of space-time change influences, effectively utilizing the wireless signal resources of the crossroad area, and improving the safety grade of a crossroad.

This invention discloses an automatic target-reporting method and system for indoor and outdoor bullet shooting. It includes veiling sensor array, signal collection, coordinates computation and display parts. The basic cell array of the veiling sensor is composed of a linear light source L and corresponding receiving device. The linear light source L is composed of light-emitting diodes that are arranged in line. The receiving device S is formed by the end face of optical fiber. The veiling sensor is three-dimensional frame structure. Two parallel veiling are used together in the X and Y computed coordinate directions. Each of them has two V shaped veiling which form alpha angle with the two parallel veiling. Thus, they form the special veiling sensor array. Only four sets of veiling basic cell arrays can fulfill the three-dimensional no touch measurement of the bullet. It can give the exact location and speed of the bullet on the target. The rate of false reporting rate is low. It will not be interfered by adjacent target.