Residual Quantity in Percentage of Factory Machines Using Computer Vision and Mathematical Methods

TL;DR

This paper explores alternative methods to deep learning for analyzing factory machines using computer vision and mathematical techniques, aiming to improve performance over traditional deep learning approaches.

Contribution

It introduces a novel approach combining computer vision with mathematical methods to assess residual quantities in factory machines, challenging reliance solely on deep learning.

Findings

Deep learning may underperform compared to manual processing in certain computer vision tasks.

Mathematical methods can enhance the accuracy of residual quantity estimation.

The proposed approach offers a viable alternative to deep learning in industrial computer vision applications.

Abstract

Computer vision has been thriving since AI development was gaining thrust. Using deep learning techniques has been the most popular way which computer scientists thought the solution of. However, deep learning techniques tend to show lower performance than manual processing. Using deep learning is not always the answer to a problem related to computer vision.