Learning Using Privileged Information for Litter Detection

TL;DR

This paper introduces a novel deep learning approach that leverages privileged information and binary mask encoding to improve litter detection accuracy across multiple models without increasing complexity.

Contribution

It is the first to combine privileged information with deep learning for litter detection and encodes bounding boxes as binary masks to enhance detection guidance.

Findings

Consistent performance improvements across all tested models.

Effective detection of small and partially obscured litter objects.

Maintains model efficiency without added complexity.

Abstract

As litter pollution continues to rise globally, developing automated tools capable of detecting litter effectively remains a significant challenge. This study presents a novel approach that combines, for the first time, privileged information with deep learning object detection to improve litter detection while maintaining model efficiency. We evaluate our method across five widely used object detection models, addressing challenges such as detecting small litter and objects partially obscured by grass or stones. In addition to this, a key contribution of our work can also be attributed to formulating a means of encoding bounding box information as a binary mask, which can be fed to the detection model to refine detection guidance. Through experiments on both within-dataset evaluation on the renowned SODA dataset and cross-dataset evaluation on the BDW and UAVVaste litter detection datasets, we demonstrate consistent performance improvements across all models. Our approach not only bolsters detection accuracy within the training sets but also generalises well to other litter detection contexts. Crucially, these improvements are achieved without increasing model complexity or adding extra layers, ensuring computational efficiency and scalability. Our results suggest that this methodology offers a practical solution for litter detection, balancing accuracy and efficiency in real-world applications.