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Application of the YOLOv5 Model for the Detection of Microobjects in the Marine Environment
Summary
Researchers trained the YOLOv5 machine learning model to automatically detect and classify microplastics and microplankton in underwater photo and video images in real time. The system achieved accuracy comparable to manual counting, offering a faster, less labor-intensive method for monitoring microplastic contamination in marine environments.
The efficiency of using the YOLOV5 machine learning model for solving the problem of automatic de-tection and recognition of micro-objects in the marine environment is studied. Samples of microplankton and microplastics were prepared, according to which a database of classified images was collected for training an image recognition neural network. The results of experiments using a trained network to find micro-objects in photo and video images in real time are presented. Experimental studies have shown high efficiency, comparable to manual recognition, of the proposed model in solving problems of detect-ing micro-objects in the marine environment.