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[2602.13681] An Ensemble Learning Approach towards Waste Segmentation in Cluttered Environment
Summary
This article presents an Ensemble Learning approach to enhance waste segmentation accuracy in cluttered environments, crucial for improving recycling processes.
Why It Matters
With environmental pollution on the rise, effective waste segregation is vital for recycling efficiency. This research leverages advanced computer vision techniques to improve the accuracy of waste classification, potentially leading to better raw material recovery and reduced human intervention in sorting processes.
Key Takeaways
- The study proposes an Ensemble Learning model combining U-Net and FPN for improved waste segmentation.
- Achieved an IoU value of 0.8306, surpassing previous models' performance.
- The approach enhances feature learning through effective preprocessing techniques.
- Improved segmentation accuracy can facilitate better recycling processes.
- The research addresses the complexities of real-world waste environments.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.13681 (cs) [Submitted on 14 Feb 2026] Title:An Ensemble Learning Approach towards Waste Segmentation in Cluttered Environment Authors:Maimoona Jafar, Syed Imran Ali, Ahsan Saadat, Muhammad Bilal, Shah Khalid View a PDF of the paper titled An Ensemble Learning Approach towards Waste Segmentation in Cluttered Environment, by Maimoona Jafar and 4 other authors View PDF HTML (experimental) Abstract:Environmental pollution is a critical global issue, with recycling emerging as one of the most viable solutions. This study focuses on waste segregation, a crucial step in recycling processes to obtain raw material. Recent advancements in computer vision have significantly contributed to waste classification and recognition. In waste segregation, segmentation masks are essential for robots to accurately localize and pick objects from conveyor belts. The complexity of real-world waste environments, characterized by deformed items without specific patterns and overlapping objects, further complicates waste segmentation tasks. This paper proposes an Ensemble Learning approach to improve segmentation accuracy by combining high performing segmentation models, U-Net and FPN, using a weighted average method. U-Net excels in capturing fine details and boundaries in segmentation tasks, while FPN effectively handles scale variation and context in complex environments, and their combined masks result in more precise predicti...