论文标题
Yolov5模型在海洋环境中检测微观对象
Application of the YOLOv5 Model for the Detection of Microobjects in the Marine Environment
论文作者
论文摘要
研究了在海洋环境中使用Yolov5机器学习模型解决自动脱离和识别微观对象的问题的效率。制备了微量浮游物和微塑料的样品，根据该样品，收集了分类图像的数据库，以训练图像识别神经网络。提出了使用训练有素的网络实时在照片和视频图像中找到微观对象的实验结果。实验研究表明，在解决海洋环境中检测到的微观对象的问题方面，提出的模型具有很高的效率，与手动识别相当。
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.
