Detection of River Plastic Using UAV Sensor Data and Deep Learning

Abstract

Plastic pollution is a critical global issue. Increases in plastic consumption have triggered increased production, which in turn has led to increased plastic disposal. In situ observation of plastic litter is tedious and cumbersome, especially in rural areas and around transboundary rivers. We therefore propose automatic mapping of plastic in rivers using unmanned aerial vehicles (UAVs) and deep learning (DL) models that require modest compute resources. We evaluate the method at two different sites: the Houay Mak Hiao River, a tributary of the Mekong River in Vientiane, Laos, and Khlong Nueng canal in Talad Thai, Khlong Luang, Pathum Thani, Thailand. Detection models in the You Only Look Once (YOLO) family are evaluated in terms of runtime resources and mean average Precision (mAP) at an Intersection over Union (IoU) threshold of 0.5. YOLOv5s is found to be the most effective model, with low computational cost and a very high mAP of 0.81 without transfer learning for the Houay Mak Hiao dataset. The performance of all models is improved by transfer learning from Talad Thai to Houay Mak Hiao. Pre-trained YOLOv4 with transfer learning obtains the overall highest accuracy, with a 3.0% increase in mAP to 0.83, compared to the marginal increase of 2% in mAP for pre-trained YOLOv5s. YOLOv3, when trained from scratch, shows the greatest benefit from transfer learning, with an increase in mAP from 0.59 to 0.81 after transfer learning from Talad Thai to Houay Mak Hiao. The pre-trained YOLOv5s model using the Houay Mak Hiao dataset is found to provide the best tradeoff between accuracy and computational complexity, requiring model resources yet providing reliable plastic detection with or without transfer learning. Various stakeholders in the effort to monitor and reduce plastic waste in our waterways can utilize the resulting deep learning approach irrespective of location.

Designing Unmanned Aerial Survey Monitoring Program to Assess Floating Litter Contamination

“…The similarity of the light reflectance vs. the spectrum between the blue sea and the translucent green of the bottles could have hampered the detection and classification of these items [34,35,96,97]. In the class Plastic Bags, as they present different shapes in each image, automated detection may have been negatively affected, as the shape of an object can be a relevant criterion for classification success [98]. The flexibility and mutable shape of Plastic Bags create a handicap for the automatic detection of this item class.…”

“…In the machine learning method, the model requires considerably more time to provide information on the number of different objects than that taken by a user to visually classify an image and tag the multiple objects (Figure 4); however, an important consideration is the fact that the classification process can mostly run with no human supervision required. Indeed, AI algorithms have already been used to automate marine litter recognition from aerial imagery, where the common algorithms applied are typically based on random forest algorithms [64,68,99] or deep learning approaches [45,46,79,98]. The main factor that encourages the development of AI algorithms for automatic identification of floating marine litter is that, after the first initial effort of classification and validation, it is a process that can be replicated for future studies without human supervision, which creates less time-consuming workflows.…”

Progress in Research on Marine Litter-Related Monitoring Technologies

“…Cortesi et al [69] used an unmanned airborne multispectral camera to monitor floating litter in the Arno River, Italy, and used a stochastic Forest Machine Learning algorithm for litter identification, which showed an accuracy of more than 98% and concluded that the infrared band is helpful in improving the identification accuracy. Maharjan et al [70] processed UAV aerial imagery of the Mekong River tributaries-Huay Mai River and Bangkok Canal for deep learning and concluded that the various YOLO Deep Learning algorithms can all exceed 80% accuracy.…”

Editorial: AI, sensors and robotics in plant phenotyping and precision agriculture, volume II

“…A good ecological environment including forest, land and water resources is the basis of sustainable development. Researchers are paying more attention to applying artificial intelligence and sensor technology to ecological systems, and making further contributions to sustainable plant protection by sensing and monitoring ecosystem ( Maharjan et al., 2022 ).…”