针对水面铝罐垃圾检测难题，构建了首个表面视角数据集并验证了高效算法。
CANSURF: An ASV-View Can Dataset and Benchmark for Detection and Tracking of Surface-Level Debris
- 构建7.3千张原始图像的铝罐数据集，经十种增强扩展至5.7万张
- 在该数据集上训练的YOLOv11性能比通用数据集提升12倍
- 适合海洋清洁机器人研发者、计算机视觉研究者使用
水面垃圾清理面临实际挑战：需在反光、波纹和部分淹没条件下远距离识别小型、高反射目标（如铝罐）。本文提出一种无人艇视觉系统及全新的表面级铝罐数据集。数据集包含约7.3k张从视频中提取的原始图像，经十类增强方法扩展至约5.7万张训练/验证图像，覆盖多样光照与水体状态。针对水面作业特性，评估了一组检测器与检测跟踪流水线。在CANSURF上训练的YOLOv11性能较通用数据集提升12倍，凸显数据集价值。实验表明，YOLOv11+ByteTrack在多目标追踪中更稳定（身份切换少）、准确率更高；而YOLOv11+SAHI虽降低全场景精度，但显著提升远距离铝罐召回率。根据单罐抓取任务需求，YOLOv11+SAHI能检测更多铝罐。此前无公开数据集聚焦于水面视角的铝罐检测，本工作填补空白并支持可复现评估。
原文摘要 · Abstract (English)
Surface-level marine debris remains a practical bottleneck for autonomous clean-up, where small, reflective targets (e.g., aluminum cans) must be detected at distance under glare, ripples, and partial submersion. This paper presents, an ASV vision system and a new surface-can dataset. The dataset comprises ~7.3k raw images extracted from videos and annotated with bounding boxes, expanded via ten augmentation types to ~57k training/validation images spanning diverse lighting and water states. A family of detector and detector-tracker pipelines tailored to surface operations were benchmarked. Training YOLOv11 on CANSURF boosts performance 12x over generic datasets, highlighting the dataset's value. Experiments show that YOLOv11+ByteTrack yields the most stable tracks (fewer identity switches) and stronger multi-object accuracy under, while YOLOv11+SAHI increases recall on far-field cans at the cost of lower precision in full-context inputs. Given the mission profile, single-can pickup with approach and grab, YOLOv11 + SAHI proves better for detecting the maximum number of cans. No prior open dataset targets aluminum cans on water from a surface-level viewpoint; this dataset fills this gap and supports reproducible evaluation.
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