Towards the in-situ Trunk Identification and Length Measurement of Sea Cucumbers via B\'ezier Curve Modelling
<|reference_start|>Towards the in-situ Trunk Identification and Length Measurement of Sea Cucumbers via B\'ezier Curve Modelling: 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.<|reference_end|>
@article{liu2024towards,
    title={Towards the in-situ Trunk Identification and Length Measurement of Sea
  Cucumbers via B\'{e}zier Curve Modelling},
    author={Shuaixin Liu, Kunqian Li, Yilin Ding, Kuangwei Xu, Qianli Jiang, Q. M.
  Jonathan Wu, Dalei Song},
    journal={arXiv preprint arXiv:2406.13951},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13951},
    primaryClass={cs.CV}
}