Waste detection in Pomerania: non-profit project for detecting waste in environment
원문 초록
Waste pollution is one of the most significant environmental issues in the modern world. The importance of recycling is well known, either for economic or ecological reasons, and the industry demands high efficiency. Our team conducted comprehensive research on Artificial Intelligence usage in waste detection and classification to fight the world's waste pollution problem. As a result an open-source framework that enables the detection and classification of litter was developed. The final pipeline consists of two neural networks: one that detects litter and a second responsible for litter classification. Waste is classified into seven categories: bio, glass, metal and plastic, non-recyclable, other, paper and unknown. Our approach achieves up to 70% of average precision in waste detection and around 75% of classification accuracy on the test dataset. The code used in the studies is publicly available online.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Tomato Multi-Angle Multi-Pose Dataset for Fine-Grained Phenotyping
Detecting Side Effects of Adverse Drug Reactions Through Drug-Drug Interactions Using Graph Neural Networks and Self-Supervised Learning
Point-LGMask: Local and Global Contexts Embedding for Point Cloud Pre-training with Multi-Ratio Masking
A Robust Feature Downsampling Module for Remote Sensing Visual Tasks
AI-based thermal bridge detection of building rooftops on district scale using aerial images
Efficient Calisthenics Skills Classification through Foreground Instance Selection and Depth Estimation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.