Residual Quantity in Percentage of Factory Machines Using Computer Vision and Mathematical Methods
This work tackles the challenge of automating residual quantity measurement in industrial settings, but it appears incremental as it critiques existing methods without presenting new results.
The paper addresses the problem of residual quantity estimation in factory machines using computer vision, noting that deep learning techniques often underperform compared to manual processing. It suggests that deep learning is not always the optimal solution for such vision-related tasks.
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.