Visual analysis of fish feeding intensity for smart feeding in aquaculture using deep learning

Su, Jui-Yuan; Zhang, Peihua; Cai, Sin-Yi; Cheng, Stone; Chang, Chin-Chun

doi:10.1117/12.2566902

Cited by 4 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A single image may not reflect the context information of fish feeding behaviour [16]. Videos can capture both spatial and temporal information on fish feeding behaviour, providing context information for fish feeding behaviour [14], [15]. Converting the original RGB video into an optical flow image sequence and then fed it into a 3D CNN is a common method for video-based FFIA, which outperforms image-based models [12], [14], [30].…”

Section: A Visual-based Ffia Methodsmentioning

confidence: 99%

Fish Feeding Intensity Assessment in Aquaculture: A New Audio Dataset AFFIA3K and a Deep Learning Algorithm

Meng

Liu

Zhao

et al. 2022

2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP)

View full text Add to dashboard Cite

Fish feeding intensity assessment (FFIA) aims to evaluate the change of fish appetite during the feeding process, which is potentially useful in industrial aquaculture. Previous methods are mainly based on computer vision techniques. However, these methods are limited by water refraction and uneven illumination. In this paper, we introduce a new approach for FFIA using audio. We create a new audio dataset for FFIA, namely AFFIA3K, which contains 3000 labelled audio clips of different fish feeding intensity (None, Weak, Medium, Strong). We present a deep learning framework for FFIA, where the audio signal is first transformed into acoustic features, i.e. mel spectrogram, then a convolutional neural network (CNN)-based model is used to classify the fish feeding intensity. Experimental results show that our approach achieves an mAP of 0.74 on the test set of AFFIA3K, and considerably outperforms baseline systems. This indicates the potential of our proposed approach in aquaculture applications.

show abstract