Feature fusion strategy and improved GhostNet for accurate recognition of fish feeding behavior

Abstract

In aquaculture, accurate detection of fish feeding intensity is a critical step for establishing an on-demand feeding system. In this paper, a novel fish feeding intensity detection method based on the fusion of multiple features (Mel spectrogram, STFT, and CQT feature map) and the LC-GhostNet lightweight network was proposed as an experimental object with Oplegnathus. First, a dataset of feeding sounds of Oplegnathus punctatus was built, in which there are four types: “strong”, “medium”, “weak” and “none”. Next, the Mel, STFT, and CQT feature maps of the feeding sound were extracted using the Librosa library, and then these feature maps were fused by feature image stitching. Lastly, the fused audio feature maps were fed to LC-GhostNet for further feature extraction and classification. Experimental results indicated that the accuracy of the fused feature maps was 97.941% when used as input, which was 4.053%, 7.207%, and 3.003% higher than that of the single feature Mel, STFT, and CQT, respectively. The method proposed in this paper is more lightweight and effective than the mainstream methods, and the accuracy has been improved. Based on this automatic and non-destructive method of obtaining fish feeding information, feeding decisions can be effectively optimized.