  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.
