  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.
