Limits...
Parallel Algorithm for GPU Processing; for use in High Speed Machine Vision Sensing of Cotton Lint Trash

View Article: PubMed Central - PubMed

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 lint fiber-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 of 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 1024×1024 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 feed-back control that is in tight cooperation with the cleaning equipment.

No MeSH data available.


Frequency response of the one-dimensional cross-section of the 2D Gaussian band pass filter detailed in figure 5.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3672999&req=5

f6-sensors-08-00817: Frequency response of the one-dimensional cross-section of the 2D Gaussian band pass filter detailed in figure 5.

Mentions: The discrete frequency response of the filter, as calculated from equation 4, via the fast Fourier-Transform (FFT) algorithm, is shown in figure 5. For clarity, the one-dimensional cross-section of the filter is shown in figure 6.


Parallel Algorithm for GPU Processing; for use in High Speed Machine Vision Sensing of Cotton Lint Trash
Frequency response of the one-dimensional cross-section of the 2D Gaussian band pass filter detailed in figure 5.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3672999&req=5

f6-sensors-08-00817: Frequency response of the one-dimensional cross-section of the 2D Gaussian band pass filter detailed in figure 5.
Mentions: The discrete frequency response of the filter, as calculated from equation 4, via the fast Fourier-Transform (FFT) algorithm, is shown in figure 5. For clarity, the one-dimensional cross-section of the filter is shown in figure 6.

View Article: PubMed Central - PubMed

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 lint fiber-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 of 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 1024×1024 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 feed-back control that is in tight cooperation with the cleaning equipment.

No MeSH data available.