pLitterStreet: Street Level Plastic Litter Detection and Mapping
This work addresses plastic pollution monitoring for environmental researchers and urban planners, but it is incremental as it applies existing methods to a new dataset.
The paper tackles the problem of detecting and mapping plastic litter at street level using vehicle-mounted cameras and deep learning, achieving an average precision above 40% with state-of-the-art object detection algorithms on their open-source dataset.
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.