# Real-Time Beach Litter Detection and Counting: A Comparative Analysis of RT-DETR Model Variants

arXiv: https://arxiv.org/abs/2508.13101 · HTML: https://mlsanity.com/paper/2508.13101/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Builds on (most cited first, 6 of 6)

- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [Sparse R-CNN: End-to-End Object Detection with Learnable Proposals](https://mlsanity.com/paper/2011.12450/) (2020)
- [VarifocalNet: An IoU-aware Dense Object Detector](https://mlsanity.com/paper/2008.13367/) (2020)

## Abstract (arXiv)

Coastal pollution is a pressing global environmental issue, necessitating scalable and automated solutions for monitoring and management. This study investigates the efficacy of the Real-Time Detection Transformer (RT-DETR), a state-of-the-art, end-to-end object detection model, for the automated detection and counting of beach litter. A rigorous comparative analysis is conducted between two model variants, RT-DETR-Large (RT-DETR-L) and RT-DETR-Extra-Large (RT-DETR-X), trained on a publicly available dataset of coastal debris. The evaluation reveals that the RT-DETR-X model achieves marginally superior accuracy, with a mean Average Precision at 50% IoU (mAP@50) of 0.816 and a mAP@50-95 of 0.612, compared to the RT-DETR-L model's 0.810 and 0.606, respectively. However, this minor performance gain is realized at a significant computational cost; the RT-DETR-L model demonstrates a substantially faster inference time of 20.1 ms versus 34.5 ms for the RT-DETR-X. The findings suggest that the RT-DETR-L model offers a more practical and efficient solution for real-time, in-field deployment due to its superior balance of processing speed and detection accuracy. This research provides valuable insights into the application of advanced Transformer-based detectors for environmental conservation, highlighting the critical trade-offs between model complexity and operational viability.