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YOLO-LFPD:一种轻量级的方法,用于检测条纹表面缺陷

Jianbo Lu¹, Mingrui Zhu², Kaixian Qin¹

¹Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning 530001, China.

Biomimetics (Basel, Switzerland)

|October 25, 2024

概括

本研究介绍了YOLO-LFPD,这是一个改进的钢带表面缺陷检测模型. 它提高了检测速度和准确性,使其适合实时工业应用.

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:

条形钢表面缺陷识别对于工业生产至关重要.
现有的方法在特征提取,检测速度和数据集限制方面面临挑战.

研究的目的:

提出一个改进的轻量级模型,以有效和准确地检测钢带表面的缺陷.
提高工业环境中的实时监控能力.

主要方法:

开发了基于YOLOv5.5的YOLO-LFPD (轻质微粒探测) 模型.
集成的RepVGG模块提高了稳定性和FasterNet作为加速推断的支柱.
采用修剪和GA遗传算法与OTA损失函数用于模型优化.

主要成果:

实现了参数减少48%和GFLOP减少13%.
与原始模型相比,推断时间减少了77%.
在NEU-DET数据集上,准确度提高了3%至81.2%,优于其他近期模型.

结论:

YOLO-LFPD模型为钢带缺陷检测提供了显著的效率和准确性的改进.
拟议的模型为轻量级的实时缺陷检测系统提供了有价值的参考.

关键词:

快速网络 (FasterNet) 是一个快速的网络.这是一个YOLO YOLO.轻量化轻量化轻量化修剪修剪修剪修剪识别表面缺陷的识别方法

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