Instance segmentation models for detecting floating macroplastic debris from river surface images

Abstract

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Quantifying the transport of floating macroplastic debris (FMPD) in waterways is essential for understanding the plastic emission from land. However, no robust tool has been developed to monitor FMPD. Here, to detect FMPD on river surfaces, we developed five instance segmentation models based on state-of-the-art You Only Look Once (YOLOv8) architecture using 7,356 training images collected via fixed-camera monitoring of seven rivers. Our models could detect FMPD using object detection and image segmentation approaches with accuracies similar to those of the pretrained YOLOv8 model. Our model performances were tested using 3,802 images generated from 107 frames obtained by a novel camera system embedded in an ultrasonic water level gauge (WLGCAM) installed in three rivers. Interestingly, the model with intermediate weight parameters most accurately detected FMPD, whereas the model with the most parameters exhibited poor performance due to overfitting. Additionally, we assessed the dependence of the detection performance on the ground sampling distance (GSD) and found that a smaller GSD for image segmentation approach and larger GSD for object detection approach are capable of accurately detecting FMPD. Based on the results from our study, more appropriate category selections need to be determined to improve the model performance and reduce the number of false positives. Our study can aid in the development of guidelines for monitoring FMPD and the establishment of an algorithm for quantifying the transport of FMPD.

Keywords

• floating macroplastic debris
• transport
• YOLOv8
• instance segmentation
• river surface
• fixed camera