TACO: Trash Annotations in Context for Litter Detection

pLitterStreet：街道层面塑料垃圾检测与制图

pLitterStreet: Street Level Plastic Litter Detection and Mapping

本研究针对塑料污染监测需求，提出基于车载街景图像的深度学习塑料垃圾与垃圾桶检测及热力图制图方法，构建含1.3万余张标注图的开源pLitterStreet数据集，验证了车载方案的可靠性，为相关模型研发提供资源。

A Generative Approach Towards Improved Robotic Detection of Marine Litter

This paper presents an approach to address data scarcity problems in underwater image datasets for visual detection of marine debris. The proposed approach relies on a two-stage variational autoencoder (VAE) and a binary classifier to evaluate the generated imagery for quality and realism. From the images generated by the two-stage VAE, the binary classifier selects "good quality" images and augments the given dataset with them. Lastly, a multi-class classifier is used to evaluate the impact of the augmentation process by measuring the accuracy of an object detector trained on combinations of …

Trashbusters：用于垃圾检测与跟踪的深度学习方法

Trashbusters: Deep Learning Approach for Litter Detection and Tracking

针对公共场所乱扔垃圾传统处置方法的诸多缺陷，研究提出融合监控摄像头与计算机视觉算法的Trashbusters全自动系统，实现垃圾检测、人员跟踪与身份识别，自动化处罚流程以提升反乱扔垃圾执法效率。

PoTATO：用于分析漂浮垃圾物体偏振迹的数据集

PoTATO: A Dataset for Analyzing Polarimetric Traces of Afloat Trash Objects

针对水面塑料垃圾检测受光照和反射影响的问题，提出含12,380个标记瓶及偏振信息的PoTATO数据集，验证偏振提升检测效果，并公开代码数据以推动算法发展。

环境无人机图像的全景分割：海滩垃圾

Panoptic Segmentation of Environmental UAV Images : Litter Beach

本文针对海滩垃圾监测中沙地异质性导致的干扰问题，提出结合实例分割与全景分割的 CNN 方法，在少量样本下实现高精度且更稳健的无人机图像垃圾识别。

利用用于垃圾分类的先进目标检测优化废物管理

Optimizing Waste Management with Advanced Object Detection for Garbage Classification

本文针对垃圾分类效率低下问题，提出利用YOLO V5目标检测模型进行垃圾识别，有效区分塑料、纸张、玻璃等多种废物，为废物管理系统和未来收集机器人提供了低人工的AI解决方案。