基于FlowCam影像和YOLOv3深度学习模型的多纹膝沟藻检测OA北大核心CSTPCD

Harmful algal blooms in the sea,including red or brown tides formed by microalgae and green or golden tides formed by macroalgae,have become a prominent marine ecological disaster.During November-December 2021,a large-scale red tide was formed by dinoflagellate Gonyaulax polygramma in the coastal waters northeast of the Shandong Peninsula,causing severe damage to the cultivated kelp Saccharina japonica.Therefore,developing efficient red tide monitoring and early-warning systems is necessary.Considering G.polygramma as the object,FlowCam was combined with convolutional neural network(CNN)model YOLOv3 in this study to analyze three simulated samples primarily containing G.polygramma,G.polygramma,and Alexandrium catenella,and live cells and theca of G.polygramma.Herein,the potential of a deep learning model for the identification of target microal-gae based on FlowCam images for detecting red tide species was explored.According to the results,YOLOv3 showed a good recognition ability for G.polygramma after training with 30,000 batches of the image datasets ob-tained using FlowCam.The average detection precision for G.polygramma in the simulated seawater sample was 88.2%.When A.catenella was present in the sample as interference,the average precision of the model decreased to 76.6%.Training and detecting both G.polygramma and A.catenella simultaneously could improve the average pre-cision in identifying G.polygramma to 84.6%.The model could identify live cells and hollow theca of G.poly-gramma in seawater,with an average precision of 86.7%for the live cells and 87.8%for theca;furthermore,the AP for identifying live cells and theca separately was higher(87.3%)than that identifying both as G.polygramma(84.2%)by 3.1%.The overall recognition accuracy had been improved.The results show that the combination of FlowCam and YOLOv3 has an important application potential in red tide monitoring and research.