A few-shot object detection method for garbage via variational autoencoders and feature aggregation

Abstract

Outdoor waste detection plays a pivotal role in environmental monitoring and waste management systems. Traditional garbage detectors rely heavily on a large amount of labelled data for training. However, these approaches are resource-intensive and ill-suited for waste categories that evolve quickly or are uncommon. To address the limitation, we propose a novel few-shot object detection (FSOD) method, named Few-Shot Garbage Detection (FSGD), which is tailored to identify garbage with limited labelled data. In the context of garbage detection, the changes in waste shapes caused by human behaviours can result in situations where the support images fail to fully represent category information. To tackle the issue, we utilize variational autoencoders (VAEs) to infer class distributions and sample robust variational features, ensuring an accurate representation of the garbage categories. Moreover, we propose an advanced aggregation strategy to establish correlations between support and query features. This strategy addresses the common problem in FSOD where the Region Proposal Network (RPN) is insensitive to novel categories. Additionally, we separate the weight of backbone network shared by support and query branches, which improves performance in a simple yet efficient way. Extensive experiments demonstrate that our method outperforms existing state-of-the-art FSOD methods in all evaluated scenarios on garbage detection datasets. Furthermore, we evaluate the generalization ability of the proposed FSGD approach on the publicly available Pascal VOC dataset, and the results indicate that FSGD also performs better than compared methods on this dataset.