不同海洋物种检测效果差异大，根源在定位难与特征复杂。
Are All Marine Species Created Equal? Performance Disparities in Underwater Object Detection
- 拆分检测任务为定位与分类，分析性能瓶颈
- 定位阶段误差最大，即使数据平衡仍存在精度差距
- 提升弱表现物种需优化算法，尤其定位模块
水下目标检测对海洋生态监测至关重要，但面临图像质量下降、类别分布不均和视觉特性独特等挑战。并非所有物种检测效果相同，但根本原因尚不明确。本文回答两个核心问题：1）除数据量外，哪些因素导致类间性能差异？2）如何系统性提升表现较差物种的检测能力？通过操纵DUO和RUOD数据集，将检测任务分解为定位与分类两部分，重点分析扇贝类的低表现问题。基于YOLO11和TIDE的定位分析表明，前景-背景区分是主要瓶颈，且不受数据量影响。分类实验显示，即使数据均衡，精度差距依然存在，说明其根源在于内在特征复杂性，而非数据稀缺或类间依赖。建议在注重精度时采用不平衡分布，在注重召回率时使用平衡分布。提升表现差物种应聚焦算法改进，特别是定位模块。代码与数据集已公开。
原文摘要 · Abstract (English)
Underwater object detection is critical for monitoring marine ecosystems but poses unique challenges, including degraded image quality, imbalanced class distribution, and distinct visual characteristics. Not every species is detected equally well, yet underlying causes remain unclear. We address two key research questions: 1) What factors beyond data quantity drive class-specific performance disparities? 2) How can we systematically improve detection of under-performing marine species? We manipulate the DUO and RUOD datasets to separate the object detection task into localization and classification and investigate the under-performance of the scallop class. Localization analysis using YOLO11 and TIDE finds that foreground-background discrimination is the most problematic stage regardless of data quantity. Classification experiments reveal persistent precision gaps even with balanced data, indicating intrinsic feature-based challenges beyond data scarcity and inter-class dependencies. We recommend imbalanced distributions when prioritizing precision, and balanced distributions when prioritizing recall. Improving under-performing classes should focus on algorithmic advances, especially within localization modules. We publicly release our code and datasets.
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