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Feature Extraction Using Deep Learning Techniques to Identify Microplastics in Open Sewer Systems
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Researchers developed a computer vision pipeline to identify microplastics in open sewer images near the Indian Ocean, using auto-cropping, bounding-box annotation with TensorFlow and OpenCV, and Scale-Invariant Feature Transform to extract shape features from 1,000 field photographs collected across three Kenyan counties.
Microplastics have been known to kill fish and other microorganisms that feed on them in water bodies. The microplastics are also harmful to human beings when consumed directly or indirectly. This paper focuses on extracting features that can be used to build a model for identifying microplastics in images taken from open sewers that lead to the Indian Ocean. One thousand (1000) pictures were taken from selected points in Kilifi, Mombasa and Kwale counties in Kenya using a still picture camera. The pictures were then subjected to auto-cropping using a code written in python programming language. TensorFlow tool with openCV was used to capture the shape of the microplastics and annotate them by drawing bounding boxes. This was followed by application of Scale-Invariant Feature Transform (SIFT) algorithm to extract features from the images. The output of the process was a dataset of features for model building to identify microplastics in images. Further research can be conducted to extract more features using different algorithms and build models for identifying microplastics in images.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.