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基于深度学习的框架,用于检测和分类有声鱼类.
Ziqi Huang1,2, Dominik Ochs3,4, M Clara P Amorim5,6
1Interactive Technologies Institute (ITI) / LARSyS, Lisbon, Portugal.
The Journal of the Acoustical Society of America
|August 13, 2025
概括
对于被动声学监测 (PAM) 的深度学习框架准确地识别鱼的声音,帮助人口评估. 基于细分的分类和物体检测方法在具有挑战性的声环境中都表现出高性能.
科学领域:
- 海洋生物学 海洋生物学
- 生物声学是一种生物声学.
- 生态学中的人工智能
背景情况:
- 被动声学监测 (PAM) 是自然环境中鱼类种群评估的一个关键工具.
- 声乐鱼的发声可能会重叠,为准确的物种识别和种群计数带来挑战.
- 深度学习为分析复杂的声学数据提供了先进的计算方法.
研究的目的:
- 为了比较两个深度学习框架对鱼声检测和分类的有效性.
- 评估性能指标,包括准确性,F1分数和推断速度.
- 评估不同的深度学习方法是否适合使用PAM进行鱼群评估.
主要方法:
- 使用CNN和LSTM实施了基于多标签细分的分类系统 (SegClas).
- 利用基于YOLO的对象检测 (ObjDet) 框架进行声音事件检测,分类和计数.
- 测试了两种框架的语音从Lusitanian青鱼,微弱,和弱鱼在泰古斯河口.
主要成果:
- 对于物种级声音识别,SegClas和ObjDet都实现了高精度 (>96%) 和F1分数 (>87%).
- ObjDet表现出略高的分类性能 (F1高达92%),并通过界限框注释实现了精确的计数.
- SegClas提供了更快的推断时间,并利用了细分级别的标签,提供了一个计算效率高的替代方案.
结论:
- 基于深度学习的PAM框架对于识别鱼类发声非常有效,即使有重叠的模式和噪音.
- 无论是ObjDet还是SegClas,都为鱼群评估提供了明显的优势,满足不同的生态和运营要求.
- 这些先进的分析工具具有显著的潜力,可以通过被动声学监测来加强鱼群评估.
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