Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes
Oussama Messai, Abbass Zein-Eddine, Abdelouahid Bentamou, Mickaël Picq, Nicolas Duquesne, Stéphane Puydarrieux, Yann Gavet
公開日: 2025/9/1
Abstract
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 de- veloped dataset comprising over 10k images and 120k in- stances. By evaluating their performance, accuracy, and com- putational 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. Addition- ally, we suggest potential future directions for further enhanc- ing 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