MLDet: Towards efficient and accurate deep learning method for Marine Litter Detection

Abstract

The relentless growth of plastic manufacturing since the 1980s has resulted in significant accumulation of litter throughout the ocean. Cleaning up this litter manually poses a major challenge, highlighting the value of autonomous underwater vehicles (AUVs) equipped with a robust vision detection algorithm. Despite prior studies, it remains challenging to employ AUVs to detect marine litter in actual maritime habitats. This paper introduces an efficient and accurate deep learning method called MLDet to cope with marine litter detection. The experimental findings show that the proposed method notably outperforms other object detectors. Furthermore, the present study comprehensively discusses the relationship between the proposed detection algorithm and recycling waste collected from marine litter. The encouraging conclusion of the study suggests that a dedicated detection algorithm is a reliable tool for automatically recognizing marine litter and sustaining a healthy ocean.