pLitterStreet: Street Level Plastic Litter Detection and Mapping
Sriram Reddy Mandhati, N. Lakmal Deshapriya, Chatura Lavanga Mendis,, Kavinda Gunasekara, Frank Yrle, Angsana Chaksan, Sujit Sanjeev

TL;DR
This paper introduces pLitterStreet, an open-source dataset and methodology for detecting and mapping street-level plastic litter using deep learning on vehicle-mounted imagery, aiding environmental monitoring.
Contribution
The paper presents a new annotated dataset and evaluates multiple object detection algorithms for plastic litter mapping in urban environments.
Findings
Average precision above 40% for detection algorithms
Vehicle-mounted cameras are effective for litter mapping
Open-source dataset facilitates further research
Abstract
Plastic pollution is a critical environmental issue, and detecting and monitoring plastic litter is crucial to mitigate its impact. This paper presents the methodology of mapping street-level litter, focusing primarily on plastic waste and the location of trash bins. Our methodology involves employing a deep learning technique to identify litter and trash bins from street-level imagery taken by a camera mounted on a vehicle. Subsequently, we utilized heat maps to visually represent the distribution of litter and trash bins throughout cities. Additionally, we provide details about the creation of an open-source dataset ("pLitterStreet") which was developed and utilized in our approach. The dataset contains more than 13,000 fully annotated images collected from vehicle-mounted cameras and includes bounding box labels. To evaluate the effectiveness of our dataset, we tested four well known…
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Taxonomy
TopicsMicroplastics and Plastic Pollution · Healthcare and Environmental Waste Management · Advanced Neural Network Applications
MethodsAverage Pooling · Residual Connection · Global Average Pooling · Convolution · 1x1 Convolution · Softmax · Focal Loss · Logistic Regression · Feature Pyramid Network · Batch Normalization
