Towards the in-situ Trunk Identification and Length Measurement of Sea Cucumbers via B\'{e}zier Curve Modelling

TL;DR

This paper presents TISC-Net, a vision-based framework combining Bézier curve modeling and YOLO for accurate in-situ identification and length measurement of sea cucumber trunks, enhancing marine resource monitoring.

Contribution

The paper introduces a novel end-to-end framework integrating Bézier curve modeling with YOLO, along with new datasets and length measurement techniques for sea cucumbers.

Findings

Achieved mAP50 above 0.9 on detection and identification tasks.

Average length measurement error around 0.15.

Effective trunk endpoint loss improves curve accuracy.

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 B\'{e}zier 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 B\'{e}zier 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 B\'{e}zier 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.