LESSDD-Net:基于特征细分和部分连接结构的轻量级和高效的钢表面缺陷检测网络.
Jiayu Wu1, Longxin Zhang1, Xinyi Pu1
1School of Computer Science and Artificial Intelligence, Hunan University of Technology, Zhuzhou 412007, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
我们开发了LESSDD-Net,这是一个用于检测钢表面缺陷的轻量级网络. 它大大降低了计算成本和模型大小,同时提高了准确性,使其适合移动设备.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 钢表面缺陷检测对于工业质量和安全至关重要.
- 目前的深度学习方法在计算上昂贵,限制了移动部署.
- 需要有效且准确的缺陷检测模型.
研究的目的:
- 提出一个轻量级和高效的钢表面缺陷检测网络 (LESSDD-Net).
- 为了减少移动应用程序的计算成本和模型大小.
- 与现有模型相比,提高检测精度.
主要方法:
- 引入了一个轻量化下方采样模块 (CSPDDM).
- 开发了一个轻量级的注意力机制 (CCAttention).
- 设计了一种轻量级的C2f模块 (LP-C2f) 以改善检测和缩小尺寸.
主要成果:
- 与YOLO11n.相比,LESSDD-Net的平均精度 (mAP) 提高了3.19%.
- 与YOLO11n.相比,模型参数减少了39.92%和计算成本减少了20.63%.
- 在主流物体检测模型中,实现了最高的检测精度,模型复杂性最低.
结论:
- LESSDD-Net为钢表面缺陷检测提供了一个高度准确和计算效率高的解决方案.
- 拟议的网络适合在手机等资源有限的设备上部署.
- 新型模块 (CSPDDM,CCAttention,LP-C2f) 助力网络的卓越性能和效率.
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