A Method for Abnormal Behavior Recognition in Aquaculture Fields Using Deep Learning

Abstract

The fish industry is an important source of income for island countries. Fish is a main source of animal-based protein. Marine fishing is gradually being replaced by marine farming (or aquaculture) due to declining wild fish populations and water pollution. However, fish farming is costly job with high requirements for labor, electricity, water, and feed. The use of deep learning to perform intelligent surveillance in aquaculture fields, reducing the need for human resources and implementing real-time monitoring, has been proposed. In this article, we propose a novel deep residual network (ResNeXt $3 \times 1 \mathrm{D}$ ) for abnormal behavior recognition in aquaculture fields. The proposed ResNeXt $3 \times 1 D$ convolutional network is mainly based on an $R(2+1) D$ convolutional network and modified to obtain better performance. Experimental results showed that the proposed ResNeXt $3 \times 1 D$ exhibited good performance for abnormal behavior recognition in aquaculture fields. Specifically, the accuracy obtained using the proposed ResNeXt $3 \times 1 \mathrm{D}$ for abnormal behavior recognition in aquaculture fields was approximately $95.3 \%$ .