Application of the YOLOv5 Model for the Detection of Microobjects in the Marine Environment
This addresses the problem of monitoring marine pollution for environmental researchers, but it is incremental as it uses an existing method on new data.
The study applied the YOLOv5 model to automatically detect and recognize microobjects like microplankton and microplastics in marine environments, achieving high efficiency comparable to manual recognition in real-time photo and video analysis.
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.