用声呐视频的时序图像实现轻量级鲑鱼计数
Counting Fish with Temporal Representations of Sonar Video
- 将数百帧声呐视频压缩为单张时序图，用轻量模型直接预测鱼群数量
- 在阿拉斯加肯尼河数据上实现23%的计数误差率
- 专为野外计算资源有限场景设计，适合无网络环境部署
准确估算鲑鱼洄游数量（即迁徙至产卵地的鱼数）是保护与渔业管理的关键数据。现有基于高分辨率成像声呐硬件的鲑鱼计数方法虽非侵入式且兼容计算机视觉处理，但依赖物体检测与跟踪技术，对许多野外部署点而言因算力和网络限制难以应用。本文提出一种轻量级计算机视觉方法，通过分析将数百帧声呐视频压缩为单张图像的回波图（echograms），直接在200帧时间窗口内预测上下游鱼数。采用ResNet-18模型，并引入领域特定图像增强和弱监督训练策略以提升性能。在阿拉斯加肯尼河代表性数据上，实现23%的计数误差，验证了该方法的可行性。
原文摘要 · Abstract (English)
Accurate estimates of salmon escapement - the number of fish migrating upstream to spawn - are key data for conservation and fishery management. Existing methods for salmon counting using high-resolution imaging sonar hardware are non-invasive and compatible with computer vision processing. Prior work in this area has utilized object detection and tracking based methods for automated salmon counting. However, these techniques remain inaccessible to many sonar deployment sites due to limited compute and connectivity in the field. We propose an alternative lightweight computer vision method for fish counting based on analyzing echograms - temporal representations that compress several hundred frames of imaging sonar video into a single image. We predict upstream and downstream counts within 200-frame time windows directly from echograms using a ResNet-18 model, and propose a set of domain-specific image augmentations and a weakly-supervised training protocol to further improve results. We achieve a count error of 23% on representative data from the Kenai River in Alaska, demonstrating the feasibility of our approach.
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