Cephalopods Classification Using Fine Tuned Lightweight Transfer Learning Models  

作　　者：P.Anantha Prabha G.Suchitra R.Saravanan 

机构地区：[1]Department of Computer Science&Engineering,Sri Krishna College of Technology,Coimbatore,641042,Tamil Nadu,India [2]Department of Electronics and Communication Engineering,Government College of Technology,Coimbatore,641013,Tamil Nadu,India [3]Department of Marine Pharmacology,Faculty of Allied Health Sciences,Chettinad Academy of Research and Education,Kelambakkam,Chengalpattu,603103,Tamil Nadu,India

出　　处：《Intelligent Automation & Soft Computing》2023年第3期3065-3079,共15页智能自动化与软计算（英文）

摘　　要：Cephalopods identification is a formidable task that involves hand inspection and close observation by a malacologist.Manual observation and iden-tification take time and are always contingent on the involvement of experts.A system is proposed to alleviate this challenge that uses transfer learning techni-ques to classify the cephalopods automatically.In the proposed method,only the Lightweight pre-trained networks are chosen to enable IoT in the task of cephalopod recognition.First,the efficiency of the chosen models is determined by evaluating their performance and comparing thefindings.Second,the models arefine-tuned by adding dense layers and tweaking hyperparameters to improve the classification of accuracy.The models also employ a well-tuned Rectified Adam optimizer to increase the accuracy rates.Third,Adam with Gradient Cen-tralisation(RAdamGC)is proposed and used infine-tuned models to reduce the training time.The framework enables an Internet of Things(IoT)or embedded device to perform the classification tasks by embedding a suitable lightweight pre-trained network.Thefine-tuned models,MobileNetV2,InceptionV3,and NASNet Mobile have achieved a classification accuracy of 89.74%,87.12%,and 89.74%,respectively.Thefindings have indicated that thefine-tuned models can classify different kinds of cephalopods.The results have also demonstrated that there is a significant reduction in the training time with RAdamGC.

关 键 词：CEPHALOPODS transfer learning lightweight models classification deep learning fish IOT 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]