Towards the in-situ Trunk Identification and Length Measurement of Sea Cucumbers via Bézier Curve Modelling

TL;DR

The paper advances a vision-based method for in-situ trunk identification and length measurement of sea cucumbers. It employs parametric Bézier curves to model trunk bending and introduces TISC-Net, an end-to-end framework that fuses Bézier curve modeling with a YOLO-based detector, enhanced by funnel activation and multi-scale attention, plus an endpoint loss to reduce curve deviations; depth information from a binocular camera enables length estimation via space-curve integration. The authors present two challenging SC-ISTI benchmark datasets with Bézier annotations and report $mAP_{50}>0.9$ for detection and trunk identification, with an average absolute relative length error around $0.15$. This approach enables accurate, in-situ trunk identification and length measurement, supporting resource monitoring and mechanized harvesting in marine environments.

Abstract

We introduce a novel vision-based framework for in-situ trunk identification and length measurement of sea cucumbers, which plays a crucial role in the monitoring of marine ranching resources and mechanized harvesting. To model sea cucumber trunk curves with varying degrees of bending, we utilize the parametric Bézier curve due to its computational simplicity, stability, and extensive range of transformation possibilities. Then, we propose an end-to-end unified framework that combines parametric Bézier curve modeling with the widely used You-Only-Look-Once (YOLO) pipeline, abbreviated as TISC-Net, and incorporates effective funnel activation and efficient multi-scale attention modules to enhance curve feature perception and learning. Furthermore, we propose incorporating trunk endpoint loss as an additional constraint to effectively mitigate the impact of endpoint deviations on the overall curve. Finally, by utilizing the depth information of pixels located along the trunk curve captured by a binocular camera, we propose accurately estimating the in-situ length of sea cucumbers through space curve integration. We established two challenging benchmark datasets for curve-based in-situ sea cucumber trunk identification. These datasets consist of over 1,000 real-world marine environment images of sea cucumbers, accompanied by Bézier format annotations. We conduct evaluation on SC-ISTI, for which our method achieves mAP50 above 0.9 on both object detection and trunk identification tasks. Extensive length measurement experiments demonstrate that the average absolute relative error is around 0.15.