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

TL;DR

This study evaluates RT-DETR models for real-time beach litter detection, comparing accuracy and speed, and finds a trade-off between model complexity and operational efficiency for environmental monitoring.

Contribution

It provides a comparative analysis of RT-DETR variants for beach litter detection, highlighting the balance between detection accuracy and computational efficiency.

Findings

RT-DETR-X slightly outperforms RT-DETR-L in accuracy.

RT-DETR-L offers faster inference time, making it more practical for real-time use.

The study informs model selection for environmental monitoring applications.

Abstract

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.