LIFRNet: A Novel Lightweight Individual Fish Recognition Method Based on Deformable Convolution and Edge Feature Learning

Abstract

With the continuous development of industrial aquaculture and artificial intelligence technology, the trend of the use of automation and intelligence in aquaculture is becoming more and more obvious, and the speed of the related technical development is becoming faster and faster. Individual fish recognition could provide key technical support for fish growth monitoring, bait feeding and density estimation, and also provide strong data support for fish precision farming. However, individual fish recognition faces significant hurdles due to the underwater environment complexity, high visual similarity of individual fish and the real-time aspect of the process. In particular, the complex and changeable underwater environment makes it extremely difficult to detect individual fish and extract biological features extraction. In view of the above problems, this paper proposes an individual fish recognition method based on lightweight convolutional neural network (LIFRNet). This proposed method could extract the visual features of underwater moving fish accurately and efficiently and give each fish unique identity recognition information. The method proposed in this paper consists of three parts: the underwater fish detection module, underwater individual fish recognition module and result visualization module. In order to improve the accuracy and real-time availability of recognition, this paper proposes a lightweight backbone network for fish visual feature extraction. This research constructed a dataset for individual fish recognition (DlouFish), and the fish in dataset were manually sorted and labeled. The dataset contains 6950 picture information instances of 384 individual fish. In this research, simulation experiments were carried out on the DlouFish dataset. Compared with YOLOV4-Tiny and YOLOV4, the accuracy of the proposed method in fish detection was increased by 5.12% and 3.65%, respectively. Additionally, the accuracy of individual fish recognition reached 97.8%.