Conodont image recognition based on transfer learning of deep residual neural network

This paper aims to explore the image recognition effect of conodonts based on deep neural network model. Eight kinds of Ordovician conodonts were selected as the research objects. 1188 photos of conodonts were taken under stereomicroscope, and 778 SEM images of conodonts were collected from published papers. The image data set was divided into training set and test set. In order to solve the problem of insufficient training samples, the methods of data augmentation of rotating, flip-ping, and filtering are used. Five residual neural network models of resnet-18, resnet-34, resnet-50, resnet-101 and res-net-152, were used to train to obtain the transfer learning model parameters. The top-1 test accuracy for the five models is 85.37%, 85.85%, 83.90%, 81.95%, and 80.00% respectively. The top-2 test accuracy is 94.63%, 94.63%, 94.15%, 93.17%, and 93.66% respectively. It was found that resnet-34 recognition has the highest test accuracy, which indicates that for the simple fossil like conodonts, increasing the depth of network does not improve the test accuracy. Selecting the appropriate depth model can not only improve the recognition accuracy, but also save computing resources. By comparing the transfer learning and retraining results of resnet-34 model, it was found that transfer learning can not only obtain high accuracy, but also quickly obtain model parameters, which is an important method for small sample fossil image recognition. The recogni-tion accuracy of conodont image under stereomicroscope is higher than that under scanning electron microscope. Integrity and similarity of conodont, camera angle, and dataset size are the main factors that affects the image recognition accuracy.

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