Impact of Camera Hardware on the Performance of Convolutional Neural Network Models in Machine Vision Applications
Session
Mechatronics, Sysem Engineering and Robotics
Description
In the rapidly growing field of artificial intelligence (AI), machine vision is an important area with applications ranging from agriculture to healthcare and improving people's quality of life. A critical factor in the effectiveness of AI models, especially in machine vision, is the complicated interplay between hardware and software parameters. This study addresses the performance metrics of a Convolutional Neural Network (CNN) model, focusing on the influence of different camera hardware. Since the CNN model used in this case is tailored for regression-based predictions, its evaluation depends on the number of predictions made over a period of time. Preliminary results highlight that camera hardware attributes can increase the prediction rate by achieving more than 10 predictions within a 15-second window, with this rate escalating over longer durations. These findings clearly indicate that the performance of the CNN model can be improved simply by selecting an appropriate camera model, without requiring changes to the CNN parameters or training datasets.
Keywords:
Convolutional Neural Network, Machine Vision, Regression
Proceedings Editor
Edmond Hajrizi
ISBN
978-9951-550-95-6
Location
UBT Lipjan, Kosovo
Start Date
28-10-2023 8:00 AM
End Date
29-10-2023 6:00 PM
DOI
10.33107/ubt-ic.2023.89
Recommended Citation
Kasemi, Roni, "Impact of Camera Hardware on the Performance of Convolutional Neural Network Models in Machine Vision Applications" (2023). UBT International Conference. 6.
https://knowledgecenter.ubt-uni.net/conference/IC/mech/6
Impact of Camera Hardware on the Performance of Convolutional Neural Network Models in Machine Vision Applications
UBT Lipjan, Kosovo
In the rapidly growing field of artificial intelligence (AI), machine vision is an important area with applications ranging from agriculture to healthcare and improving people's quality of life. A critical factor in the effectiveness of AI models, especially in machine vision, is the complicated interplay between hardware and software parameters. This study addresses the performance metrics of a Convolutional Neural Network (CNN) model, focusing on the influence of different camera hardware. Since the CNN model used in this case is tailored for regression-based predictions, its evaluation depends on the number of predictions made over a period of time. Preliminary results highlight that camera hardware attributes can increase the prediction rate by achieving more than 10 predictions within a 15-second window, with this rate escalating over longer durations. These findings clearly indicate that the performance of the CNN model can be improved simply by selecting an appropriate camera model, without requiring changes to the CNN parameters or training datasets.