Detecting Algorithm Using PSO For Visual Object Under Complex Industrial Environments

With the development of information technology, machine vision, as a new detecting and control method, has been applied in many industrial fields. Machine vision is the best trade-off between high accuracy control and low cost under complex industrial environments such as high temperature, dust, harsh EMI, and so forth.Vibration, dust and no fixed lighting would lead the CCD image degradation in harsh industrial conditions. The machine vision system under industrial environments should detect the objects accurately and rapidly. Therefore, it is one of the hot issues to develop a fast, precision detecting algorithm based on vision under complex industrial conditions.In this dissertation, the research is focused on the object detecting problems under complex industrial environments, and the billet detecting and locating control in kiln is as a background research project. The main works are as follows:The Otsu method is one of very efficient thresholding method for the gray image. However, its computation would become more complex in the case of multilevel thresholding. A multilevel thresholding algorithm that is based on particle swarm optimization is developed to solve the complexity of the Otsu method in multilevel thresholding. The experimental results show that the PSO-Otsu can provide better effectiveness on experiments of image segmentation.In order to improve the performance of PSO, an improved PSO using a local searching operator is proposed. The several benchmark functions are used to testify, and the results show the improved PSO has a better performance. A FCM image segmentation based on the improved PSO is proposed to improve the performance of FCM. The experimental results show that the hybrid optimization scheme can provide better effectiveness for image segmentation.The background illumination would change under harsh industrial conditions, and lead to the gray change of pixel in a background image region. The vision system would extract incorrect object information if the illumination changes are not detected. A simple detecting method is proposed to detect the background illumination changes by detecting the change of mean gray value in the local characteristic regions between two sensed images.There is not a fixed lighting in some complex industrial conditions, and the illumination changes would change with the industrial conditions changes. A multi background images model is proposed to adapt to the illumination changes under complex industrial environments. The multiple background images model is extracts from the background video in the processing of off-line learning, and swaps the background image, which is the most similar image to the current sensed image in the multi-background images, by detecting the illumination changes and matching background images using PSO.The heating kiln plays an important role in steel milling in the Steel Mill. The vision billet location control system has some problems in the billet location control, such as the low location precision, the high variation of location deviation, and so on. The multi background images detecting method using PSO is applied in the location control of billets. The result shows PSO multi background images detecting method can improve the precision of the vision billet location control system.