Abstract
Marine biodiversity monitoring requires scalability and reliability across complex underwater environments to support conservation and invasive-species management. Yet existing detection solutions often exhibit a pronounced deployment gap, with performance degrading sharply when transferred to new sites. This work establishes the foundational detection layer for a multi-year invasive species monitoring initiative targeting Arctic and Atlantic marine ecosystems. We address this challenge by developing a Unified Information Pipeline that standardises heterogeneous datasets into a comparable information flow and evaluates a fixed, deployment-relevant detector under controlled cross-domain protocols. Across multiple domains, we find that structural factors, such as scene composition, object density, and contextual redundancy, explain cross-domain performance loss more strongly than visual degradation such as turbidity, with sparse scenes inducing a characteristic "Context Collapse" failure mode. We further validate operational feasibility by benchmarking inference on low-cost edge hardware, showing that runtime optimisation enables practical sampling rates for remote monitoring. The results shift emphasis from image enhancement toward structure-aware reliability, providing a democratised tool for consistent marine ecosystem assessment.
Abstract (translated)
海洋生物多样性监测需要在复杂的水下环境中具备可扩展性和可靠性，以支持保护和入侵物种管理。然而，现有的检测解决方案通常存在明显的部署差距，在转移到新地点时性能会急剧下降。这项工作为一项多年期的入侵物种监控倡议建立了基础检测层，目标是北极和大西洋海洋生态系统。我们通过开发一个统一的信息管道来应对这一挑战，该管道将异构数据集标准化为可比较的信息流，并在受控跨领域协议下评估固定、与部署相关的检测器。我们发现，在多个领域中，结构因素如场景构成、物体密度和上下文冗余比视觉退化（例如浑浊）更强烈地解释了跨域性能的损失，并且稀疏场景会引发一个特定的“上下文崩溃”失败模式。 为了验证操作可行性，我们在低成本边缘硬件上进行了推理基准测试，结果表明运行时优化能够使远程监控的实际采样率变得可行。这些成果将重点从图像增强转移到结构感知可靠性，提供了用于一致评估海洋生态系统的民主化工具。
URL
https://arxiv.org/abs/2601.13975
