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Scientific studies on species composition and abundance distribution of fishes have considerable importance to the fishery industry, biodiversity protection, and marine ecosystem. In these studies, fish images are typically collected with the help of scuba divers or autonomous underwater vehicles. These images are then annotated manually by marine biologists. Such a process is certainly a tremendous waste of manpower and material resources. In recent years, the introduction of deep learning has helped making remarkable progress in this area. However, fish image classification can be considered as fine-grained problem, which is more challenging than common image classification, especially with low-quality and small-scale data. Meanwhile, well-known effective convolutional neural networks (CNNs) consistently require a large quantity of high-quality data. This paper presents a new method by improving transfer learning and squeeze-and-excitation networks for fine-grained fish image classification on low-quality and small-scale datasets. Our method enhances data augmentation through super-resolution reconstruction to enlarge the dataset with high-quality images, pre-pretrains, and pretrains to learn common and domain knowledge simultaneously while fine-tuning with professional skill. In addition, refined squeeze-and-excitation blocks are designed to improve bilinear CNNs for a fine-grained classification. Unlike well-known CNNs for image classification, our method can classify images with insufficient low-quality training data. Moreover, we compare the performance of our method with commonly used CNNs on small-scale fine-grained datasets, namely, Croatian and QUT fish datasets. The experimental results show that our method outperforms popular CNNs with higher fish classification accuracy, which indicates its potential applications in combination with other newly updated CNNs.

Improving Transfer Learning and Squeeze- and-Excitation Networks for Small-Scale Fine-Grained Fish Image Classification