ECR-MobileNet:一个不平衡的大嘴巴低音参数预测模型与自适应的对比回归和依赖图剪裁
Hao Peng1, Cheng Ouyang1, Lin Yang1
1College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China.
Animals : an open access journal from MDPI
|August 28, 2025
概括
这项研究介绍了ECR-MobileNet,这是一种用于水产养殖中的非破坏性鱼类测量深度学习模型. 它在预测鱼的长度和重量方面具有很高的准确性,同时在边缘部署时保持轻量级.
科学领域:
- 水产养殖技术
- 计算机视觉
- 深度学习
背景情况:
- 精确的,非破坏性的鱼长度和体重监测对于智能水产养殖至关重要.
- 传统的方法导致压力和产量损失; 目前的计算机视觉模型在数据不平衡和模型大小方面扎.
研究的目的:
- 开发一个高效和强大的深度学习框架,用于精确的,非破坏性的鱼类生物识别监测.
- 解决水产养殖计算机视觉中的数据不平衡和模型轻量化问题.
主要方法:
- 提出ECR-MobileNet,一个基于MobileNetV3-Small的轻量级框架.
- 集成的高效通道注意力 (ECA) 模块,自适应多尺度对比回归 (AMCR) 损失函数,以及基于依赖图 (DepGraph) 的结构修剪.
- 在多场景大鱼数据集上进行训练和评估.
主要成果:
- 经过修改的ECR-MobileNet-P模型的表现超过了14个基准.
- 达到了0.9784 (长度) 和0.9740 (重量) 的R2,RMSE很低.
- 模型的效率很高:0.52M参数,0.07GFLOPs,10.19ms的CPU延迟,证明了帕雷托的最佳性.
结论:
- 对于水产养殖生物识别监控,ECR-MobileNet提供了一个边缘部署的无压力解决方案.
- 提出了不平衡回归和以任务为导向的模型压缩的创新方法.
- 为推进智能水产养殖系统建立了新的方法范式.
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