WasteNet: A novel multi-scale attention-based U-Net architecture for waste detection in UAV images

Abstract

Waste littering contributes to various ecological, aesthetic, economic, and social hazards. Given the dispersed nature of littered waste in public spaces, there is a need for a wider area of inspection to enable efficient detection of such waste. The study re-emphasizes the utility of UAVs for round-the-clock public space monitoring. The application of UAVs provides constant vigil over large public spaces. However, detecting the UAV-captured objects with precision is challenging as the far-off view further reduces the size of the target waste. Moreover, the waste objects have a multi-scale nature with different shapes, textures, and compositions. Given these varied challenges, this paper proposes a novel multi-scale attention-based deep network architecture (WasteNet) for efficient semantic segmentation of UAV-collected images to detect waste objects. Attention-based multi-scale fusion blocks (ABFBs) are introduced to improve semantic segmentation performance. These ABFBs can learn the contribution of different convolution scales during the training phase. The performance of the proposed WasteNet has been validated against seven other existing state-of-the-art waste localization methods. Moreover, an ablation study has been performed to derive the best architecture. The experimental results witnessed that WasteNet can detect litter from UAV-based images with a precision of 0.99, recall of 0.99, and MAP@50 of 0.95. For a better understanding of the results, the visuals reflecting an improved precision of waste boundary identification have also been included.