LSD-YOLOv5:基于轻量网络和增强功能融合模式的钢带表面缺陷检测算法
Huan Zhao1, Fang Wan1, Guangbo Lei1
1School of Computer Science, Hubei University of Technology, Wuhan 430068, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
本研究介绍了LSD-YOLOv5,这是一种用于检测钢带表面缺陷的轻量级模型. 它显著减少了模型大小,提高了检测速度,同时提高了准确性,优化了金中的质量控制.
科学领域:
- 金质量控制 金质量控制
- 计算机视觉用于工业检查.
背景情况:
- 精确检测金属材料表面缺陷对于质量控制至关重要.
- 现有的缺陷检测模型往往受到很大的参数数量和低于最佳的检测率的影响.
研究的目的:
- 开发一种轻量级和高效的模型,用于识别钢带的表面损伤.
- 改进现有的缺陷检测方法的性能,特别是YOLOv5s.
主要方法:
- 提出了一种新的轻量级识别模型,LSD-YOLOv5,包含一个浅功能增强模块.
- 在MobileNetV2瓶内整合了协调注意机制,并引入了一个较小的双向特征金字塔网络 (BiFPN-S).
- 利用软-DIoU-NMS算法,在重叠的目标场景中提高识别效率.
主要成果:
- 与YOLOv5s相比,LSD-YOLOv5实现了61.5%的模型参数减少.
- 在检测速度方面表现出了28.7%的改善.
- 在保持轻量结构的同时,识别精度提高了2.4%.
结论:
- 对于钢带表面缺陷检测,LSD-YOLOv5提供了检测准确度,速度和模型大小之间的最佳平衡.
- 该模型为金应用中的自动化质量控制带来了重大进步.
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