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
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
state-of-the-art object detection algorithms (Faster R-CNN, RetinaNet, YOLOv3,
and YOLOv5), achieving an average precision (AP) above 40%. While the results
show average metrics, our experiments demonstrated the reliability of using
vehicle-mounted cameras for plastic litter mapping. The "pLitterStreet" can
also be a valuable resource for researchers and practitioners to develop and
further improve existing machine learning models for detecting and mapping
plastic litter in an urban environment. The dataset is open-source and more
details about the dataset and trained models can be found at
https://github.com/gicait/pLitter.