In the face of increasingly severe air target threats, laser weapons have the advantages of fast attack speed, flexible steering, precision strikes and immunity from electromagnetic interference. They are new concept weapons that are developed by the navies of various countries. At present, laser weapons strike targets, relying on manual point selection to directional strike damage points, which takes a long time, and it is easy to miss the time of damage. In order to solve the timeconsuming problem of manual point selection, this paper proposes an automatic extraction method for damage points of laser weapons. This method divides the missile target by K_means color clustering, and then fits the center line of the missile according to the pixel position index of the missile target area, combined with the least square method, and finally extracts the damage point according to the proportion of each part in the missile. This method can accurately provide damage points in real time and improve the combat effectiveness of laser weapons
Fixed-point attack on the key parts of small aerial vehicles is an important means of UAV (Unmanned Aerial Vehicle) countermeasure. Because of the fast speed and flexible attitude of fixed-wing aircraft, the detection accuracy of key points of fixed-wing aircraft in infrared images is low and the speed is slow. This paper presents an improved detection and tracking algorithm based on SVM. Firstly, the detection module extracts the fixed-wing aircraft area by image segmentation, then extracts the characteristics of the fixed-wing aircraft, then uses SVM to judge the flight direction of the fixed-wing aircraft, and then locates the key points according to the direction. The experimental results show that the proposed detection algorithm can process 30 frames per second on the platform of DSP (TSM320C6678), and still achieve a high detection rate (<93%) with very high practical value.
KEYWORDS: Clouds, Confocal microscopy, Coating, Light emitting diodes, Data acquisition, Distance measurement, Solids, Data conversion, 3D acquisition, 3D image processing
Traditional measuring equipments and methods cannot satisfy the requirements of micrometer-level accuracy and realtime measurement of LED tape coating, the paper proposes a three-dimensional measurement method to compute the thickness of LED tape coating based on linear array spectral confocal. Firstly, the distance data is collected by linear array spectral confocal scanning and converted into 3D point cloud data, then the point cloud is materialized and smoothed to make the 3D object more realistic. Finally, the 3D entity is interacted in the Point Cloud Library to perform manual measurement of the tiny parts of the object. The subsequent automatic measurements are used to control the grating ruler for the specified position moving of measurement based on the previous manual measurement processes and the procedure file. The experimental results indicate that the accuracy of the proposed measurement method is less than 3um, and automatic measurement costs the processing time within 2.5s. In addition, the measurement accuracy is as high as 99.9%, which indicates that the proposed method performs a competitive result.
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