A multilevel lightweight fish respiratory frequency measurement method – Segmentation instead of detection

Abstract

Respiratory frequency is an important physiological parameter for fish health. Traditional respiration detection methods have inherent frequency measurement error problems, and segmentation provides an innovative technique for respiration measurement. By shifting the focus from detection to segmentation, this approach provides robust and reliable support for measuring fish respiratory frequency accurately. However, the existing segmentation network models face challenges related to their large scale, high computational demands, and limited deployability at the edge. To overcome these challenges, this paper introduces an easily portable, high-precision segmentation method based on lightweight network and network slimming technique, named PM-YOLOv5s-seg (Prune MobileNetv3-YOLOv5s Segmentation). PM-YOLOv5s-seg utilizes the MobileNetv3 network as its segmentation backbone, enabling the extraction of essential fish mouth features. Simultaneously, it employs depth separable convolution to reduce network parameters and calculation amounts. Furthermore, the model undergoes additional sparsification via channel pruning, significantly improving its efficiency. Finally, respiratory frequency modeling is performed on the obtained fish mouth segmentation results, utilizing innovative techniques such as Ellipse fitting, Kalman filtering, and polynomial fitting methods. Comparison with the state-of-the-art methods demonstrates the superiority of the multilevel lightweight segmentation model PM-YOLOv5s-seg in terms of detection accuracy, computational efficiency, and model size. This model achieves an optimal balance between the speed and accuracy in fish respiratory frequency measurement, which holds significant implications for fish welfare as well as disease detection.