Automatic Detection of Microplastics in the Aqueous Environment

Abstract

Microplastics (<5 mm) have become a global concern due to their growing threat to the marine and freshwater environment. There is a lack of technologies for the rapid and accurate identification and quantification of microplastics in the aqueous environment. This paper presents a deep-learning-based methodology for real-time detection, tracking, and counting of microplastics in freshwater environments through real-time object detection. A prototype was developed to detect microplastics of 1 mm to 5 mm in size and different shapes (e.g., spherical) and colors (e.g., red, green, blue). The microplastics detection model employed the small YOLOv5 architecture as we focused on low-power applications. In-situ image collection was performed using a Logitech C270 camera, and the microplastics were manually annotated on those images before being applied for model training. For real-time object tracking, we used Simple Online and Real-time Tracking with a Deep Association Metric (DeepSORT), an extended version of the Simple Online and Real-time Tracking (SORT) algorithm. Our developed system can work up to 34 cm/sec of water velocity and successfully detect, track, count, and calculate the velocity of microplastic of size 5mm.