机器视觉检测技术机器应用研究/Machine Vision Inspection and Its Engineering Application
Machine vision technology is a new research field and developing fast. It is one of the most important fields of computer science and is attaching importance to many countries. Machine vision has been well developing in abroad, but it is still in its infancy in China. With the fast development of industry, machine vision technology has become a hot point research field of industry in our country. The scope of graphics display technology is also increasing. It applies in many areas of the national economy.
The thesis summarized the basic algorithms of machine vision and described their principle, method and technology by two projects: “Target Detection and Tracking Technology of Simulating Spacecraft Rendezvous System” and “Fault Detection and Fault Grouping of Visual PCB Soldering Inspection”. These algorithms were effectively organized to solve practical problems.
In the project “Target Detection and Tracking Technology of Simulating Spacecraft Rendezvous and Docking System”, As for the problem that the light of the environment is unstable, the thesis designed two tracking program – tracking diode and tracking white scrip and HSV color instead of RGB color was used for image processing to enhance the system’s adaptive ability. As for the problem that the computation of dynamic target tracking is so large and the tracking result is often affected by pipeline border noise, the thesis adopted adaptive background difference method to detect the target, used kNN(k-Nearest Neighbor) classification method to distinguish two similar objects, and used a mobile pipeline wave filtering tracking algorithm to solve target tracking. The methods above effectively resolved the problems and improved the real-time processing function and anti-jamming ability of the system.
In the project “Fault Detection and Fault Grouping of Visual PCB Soldering Inspection”, as for the given image sample is small and various features are serious cross, the paper analyzed the PCB soldering image, extracted the features of the image, reduced feature dimension and classified the soldering with the SVM method basing on small samples and nonlinear character.
Image processing and target tracking technology of machine vision was applied to the project “Target Detection and Tracking Technology of Simulating Spacecraft Rendezvous and Docking System”, so that the algorithm processing just spent 0.05s and the target moving at a speed of 0.2m/s could be tracked, which met the project demand that the algorithm processing time less than 0.2s and the speed of target greater than 0.01m/s. Image processing and pattern recognition technology was applied to the project “Fault Detection and Fault Grouping of Visual PCB Soldering Inspection”, so that the algorithm processing spent less than 0.05s and the correct classification rate reached 96.57%, which met the project demand that the algorithm 0.5s and the correct classification rate 90%.