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Submitted to: Sensors
Publication Type: Peer Reviewed Journal Publication Acceptance Date: 1/31/2008 Publication Date: 2/8/2008 Citation: Pelletier, M.G. 2008. Parallel algorithm for GPU processing; for use in high speed machine vision sensing of cotton lint trash. Sensors. 8:817-829. Interpretive Summary: One of the main hurdles standing in the way of optimal cleaning of lint in cotton gins is the lack of rapid sensing systems for the detection of the amount of trash contamination. Without suitable sensing systems, the operators are forced to set the machines to over-clean the cotton which results in higher fiber damage as well as the significant loss of the valuable lint. This research examines the use of programmable graphic processing units (GPU) as an alternative to the PC’s traditional use of the central processing unit (CPU). In order to take advantage of the highly parallel architechture of the GPU, the research also required the development of a new parallel algorithm to run on the GPU. The results of the combination of the new algorithm and computing platform resulted in a substantial improvement of the image processing speeds for identification of the trash content in cotton lint. By improving the processing time, rapid trash sensing systems can alleviate the situation in which the current systems view the cotton lint either well before, or after, the cotton is cleaned. This extended lag/lead time that is currently imposed on the cotton trash cleaning control systems is what is responsible for system operators utilizing a very large dead-band safety buffer in order to ensure that the cotton lint is not under-cleaned. By providing a much faster computing platform, future cotton trash imaging sensors will be able to reduce the size of the dead-band buffer which will minimize the amount of cotton lint being over-cleaned, which in turn will reduce fiber-damage and should allow the new systems utilizing this technology to approach upwards of 30% reductions in lint loss. Technical Abstract: One of the main hurdles standing in the way of optimal cleaning of cotton lint is the lack of sensing systems that can react fast enough to provide the control system with real-time information as to the level of trash contamination of the cotton lint. This research examines the use of programmable graphic processing units (GPU) as an alternative to the PC's traditional use of the central processing unit (CPU). The use of the GPU, as an alternative computation platform, allowed for the machine vision system to gain a significant improvement in processing time. By improving the processing time, this research seeks to address the lack of availability of rapid trash sensing systems, and thus alleviate a situation in which the current systems view the cotton lint either well before, or after, the cotton is cleaned. This extended lag/lead time that is currently imposed on the cotton trash cleaning control systems is what is responsible for system operators utilizing a very large dead-band safety buffer in order to ensure that the cotton lint is not under-cleaned. Unfortunately, the utilization of a large dead-band buffer results in the majority of the cotton lint being over-cleaned, which in turn causes fibre-damage as well as significant losses of the valuable lint due to the excessive use of cleaning machinery. This research estimates that upwards of a 30% reduction in lint loss could be gained through the use of a tightly coupled trash sensor to the cleaning machinery control systems. This research seeks to improve processing times through the development of a new algorithm for cotton trash sensing that allows for implementation on a highly parallel architecture. Additionally, by moving the new parallel algorithm onto an alternative computing platform, the graphic processing unit "GPU" for processing of the cotton trash images a speed up to over 6.5 times over optimized code running on the PC's central processing unit "CPU" was gained. The new parallel algorithm operating on the GPU was able to process a 1024x1024 image in less than 17ms. At this improved speed, the image processing system's performance should now be sufficient to provide a system that would be capable of real-time feedback control that is in tight cooperation with the cleaning equipment. |
