A DEEP LEARNING MODEL FOR MICROPLASTICS DETECTION IN OPEN SEWER SYSTEMS

Abstract

Microplastics (MPs) are small plastic particles that pose a threat to aquatic organisms and human health. Detecting MPs in bodies of water is critical for controlling their flow and limiting their negative effects. This study proposes a Deep Learning algorithm for detecting MPs in photos taken from open sewer systems that flow into the ocean. The research adopted the Sample, Explore, Modify, Model, and Assess (SEMMA) framework, a comprehensive data mining process. A dataset comprising 1000 photos was constructed from locations in Kilifi, Mombasa, and Kwale counties in Kenya, by employing the Scale-Invariant Feature Transform (SIFT) algorithm for feature extraction. The researchers compared the performance of two object detection models: Sing-Shot Detector (SSD), and Convolutional Neural Network (CNN), and discovered that SSD performed best with a mean average precision (mAP) score of 100%, while CNN performed worst with 96.5%. A model for detecting MPs in photos taken from open sewer systems that flow into the Indian Ocean along Kenya's coast was developed using the best-performing SSD model. The model can be used to detect MPs in other open sewer systems, assisting in the implementation of effective management and control measures. Future research could look into creating a mobile app that captures images and provides information about MPs in open sewer systems