构建公开鱼种细粒度分析数据集，助力可持续渔业管理
AutoFish: Dataset and Benchmark for Fine-grained Analysis of Fish
- 采集454条视觉相似鱼类图像，标注实例分割与长度
- 最佳模型在无遮挡下长度估计误差仅0.62厘米
- 适用于鱼类识别、长度测量及自动化渔情监测
自动化鱼类记录有望在未来在可持续渔业管理及应对过度捕捞挑战中发挥关键作用。本文提出一个公开可用的新数据集AutoFish，用于细粒度鱼类分析。该数据集包含1500张图像，涵盖454个鱼类标本，它们在白色传送带上以不同排列方式放置，并由人工标注实例分割掩码、编号和长度。数据在受控环境下通过RGB相机采集，标注过程结合手动点标注、Segment Anything Model（SAM）生成的初始掩码以及后续的人工修正。我们采用两种Mask2Former变体建立实例分割基线，最佳模型达到89.15%的mAP。此外，提出两种长度估计基线方法，最优的基于MobileNetV2的回归模型在无遮挡图像中实现0.62厘米的平均绝对误差（MAE），遮挡情况下为1.38厘米。
原文摘要 · Abstract (English)
Automated fish documentation processes are in the near future expected to play an essential role in sustainable fisheries management and for addressing challenges of overfishing. In this paper, we present a novel and publicly available dataset named AutoFish designed for fine-grained fish analysis. The dataset comprises 1,500 images of 454 specimens of visually similar fish placed in various constellations on a white conveyor belt and annotated with instance segmentation masks, IDs, and length measurements. The data was collected in a controlled environment using an RGB camera. The annotation procedure involved manual point annotations, initial segmentation masks proposed by the Segment Anything Model (SAM), and subsequent manual correction of the masks. We establish baseline instance segmentation results using two variations of the Mask2Former architecture, with the best performing model reaching an mAP of 89.15%. Additionally, we present two baseline length estimation methods, the best performing being a custom MobileNetV2-based regression model reaching an MAE of 0.62cm in images with no occlusion and 1.38cm in images with occlusion. Link to project page: https://vap.aau.dk/autofish/.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。