Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes
It tackles computer vision challenges for industrial recycling processes, but appears incremental with analysis of existing methods and a new dataset.
This paper addresses the detection of small, dense, overlapping objects and image quality enhancement in industrial recycling, using a new dataset of over 10k images and 120k instances to evaluate supervised deep learning methods and proposing a lightweight convolutional network for noise reduction.
This paper tackles two key challenges: detecting small, dense, and overlapping objects (a major hurdle in computer vision) and improving the quality of noisy images, especially those encountered in industrial environments. [1, 2]. Our focus is on evaluating methods built on supervised deep learning. We perform an analysis of these methods, using a newly developed dataset comprising over 10k images and 120k instances. By evaluating their performance, accuracy, and computational efficiency, we identify the most reliable detection systems and highlight the specific challenges they address in industrial applications. This paper also examines the use of deep learning models to improve image quality in noisy industrial environments. We introduce a lightweight model based on a fully connected convolutional network. Additionally, we suggest potential future directions for further enhancing the effectiveness of the model. The repository of the dataset and proposed model can be found at: https://github.com/o-messai/SDOOD, https://github.com/o-messai/DDSRNet