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.

翻译：研究了利用YOLOV5机器学习模型解决海洋环境中微型物体自动脱钩和识别问题的效率;研究了微型浮游生物和微塑料样本,根据这些样本收集了机密图像数据库,以培训图像识别神经网络;介绍了利用经过培训的网络实时在照片和录像图像中找到微型物体的实验结果;实验研究表明,在解决海洋环境中探测微型物体问题时,与人工识别相比,拟议的模型效率很高。