Searching for Uncollected Litter with Computer Vision
This study combines photo metadata and computer vision to quantify where uncollected litter is present. Images from the Trash Annotations in Context (TACO) dataset were used to teach an algorithm to detect 10 categories of garbage. Although it worked well with smartphone photos, it struggled when trying to process images from vehicle mounted cameras. However, increasing the variety of perspectives and backgrounds in the dataset will help it improve in unfamiliar situations. These data are plotted onto a map which, as accuracy improves, could be used for measuring waste management strategies and quantifying trends.
Code (0)
등록된 구현이 없습니다.
Tasks
ManagementSimilar Papers 제목 키워드 기반
A Computer Vision System to Localize and Classify Wastes on the Streets
Littering quantification is an important step for improving cleanliness of cities. When human interpretation is too cumbersome or in some cases impossible, an objective index of cleanliness could reduce the littering by …
BenchmarkingSentence Splitter: Uncovering Latent Factual Structure for Self-Supervised Learning
This paper introduces Sentence Splitter, a self-supervised framework built upon a T5-based encoder--decoder architecture for uncovering the latent factual structure of natural language sentences. The proposed method iden…
Knowledge Graph CompletionSelf-Supervised LearningQuestion AnsweringTrashbusters: Deep Learning Approach for Litter Detection and Tracking
The illegal disposal of trash is a major public health and environmental concern. Disposing of trash in unplanned places poses serious health and environmental risks. We should try to restrict public trash cans as much a…
Deep LearningFace RecognitionObject TrackingAI for Green Spaces: Leveraging Autonomous Navigation and Computer Vision for Park Litter Removal
There are 50 billion pieces of litter in the U.S. alone. Grass fields contribute to this problem because picnickers tend to leave trash on the field. We propose building a robot that can autonomously navigate, identify, …
RoLID-11K: A Dashcam Dataset for Small-Object Roadside Litter Detection
Roadside litter poses environmental, safety and economic challenges, yet current monitoring relies on labour-intensive surveys and public reporting, providing limited spatial coverage. Existing vision datasets for litter…
Object Detection