# Image Segmentation and Classification of E-waste for Training Robots for Waste Segregation

arXiv: https://arxiv.org/abs/2506.07122 · HTML: https://mlsanity.com/paper/2506.07122/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 6 references that are in the map.
- First posted on arXiv 8 Jun 2025.

## Builds on (most cited first, 2 of 2)

- [TACO: Trash Annotations in Context for Litter Detection](https://mlsanity.com/paper/2003.06975/) (2020)
- [EWasteNet: A Two-Stream Data Efficient Image Transformer Approach for E-Waste Classification](https://mlsanity.com/paper/2311.12823/) (2023)

## Abstract (arXiv)

Industry partners provided a problem statement that involves classifying electronic waste using machine learning models that will be used by pick-and-place robots for waste segregation. This was achieved by taking common electronic waste items, such as a mouse and charger, unsoldering them, and taking pictures to create a custom dataset. Then state-of-the art YOLOv11 model was trained and run to achieve 70 mAP in real-time. Mask-RCNN model was also trained and achieved 41 mAP. The model can be integrated with pick-and-place robots to perform segregation of e-waste.