| dc.description.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 | en_US |
