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根据YOLOv8-LA模型的水下小型目标检测
Shenming Qu1, Can Cui1, Jiale Duan1
1School of Software, Henan University, Kaifeng, 475004, Henan, China.
Scientific reports
|July 12, 2024
概括
一个新的YOLOv8-LA模型使用高效的神经网络增强了水下目标检测. 该方法实现了高精度和实时处理,优于海洋环境工程应用的现有技术.
科学领域:
- 海洋环境工程 海洋环境工程
- 计算机视觉 计算机视觉 计算机视觉
- 深度学习是一种深度学习.
背景情况:
- 准确的水下目标检测对于海洋环境工程至关重要.
- 现有的卷积神经网络 (CNN) 模型面临着缓慢的处理速度和检测小,密集的目标的挑战.
- 深度神经网络通常需要广泛的参数,阻碍实时应用程序.
研究的目的:
- 提出一个改进的神经网络模型,YOLOv8-LA,用于增强水下目标检测.
- 为了解决处理速度和精度的局限性,用于小型,密集的水下目标.
- 优化特征提取和信息聚合,以获得卓越的检测性能.
主要方法:
- 开发一个轻量高效的部分卷积 (LEPC) 模块,以优化空间特征提取和减少计算.
- 集成AP-FasterNet架构,利用深度可分离的卷积,以改善小目标特征的捕获.
- 将内容意识重组 (CARAFE) 提升样本操作纳入YOLOv8,以增强上下文信息聚合并最大限度地减少信息丢失.
主要成果:
- 在URPC2021数据集上,YOLOv8-LA模型实现了84.7%的平均精度 (mAP).
- 该模型在Nvidia GeForce RTX 3090.0上展示了每秒189.3 (FPS) 的处理速度.
- 在检测准确度和处理速度方面,YOLOv8-LA的性能超过了现有的最先进的方法.
结论:
- YOLOv8-LA模型在水下目标检测方面取得了重大进展.
- 拟议的模型有效地平衡了高检测精度与实时处理能力.
- 这项研究为需要高效准确的水下监测的海洋环境工程应用提供了强大的解决方案.
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