一个多层次的注意机制,用于检测皮革织物的缺陷
Hao Li1, Yifan Liu1, Huawei Xu2
1School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, 430048, China.
Heliyon
|September 2, 2024
概括
这项研究引入了一种新的多层残留卷积注意 (MLRCA) 方法,用于自动皮革缺陷检测. MLRCA方法显著提高了在工业质量控制中识别轻微缺陷的准确性.
科学领域:
- 工业工程 工业工程 工业工程
- 计算机视觉 计算机视觉
- 材料科学 材料科学 材料科学
背景情况:
- 缺陷检测对于皮革生产的质量控制至关重要.
- 挑战包括不同的缺陷大小,位置和微妙的类间变化.
研究的目的:
- 开发一种先进的自动化方法,用于准确检测皮革缺陷.
- 为了提高识别小和类似的缺陷类型.
主要方法:
- 提出了一种多层剩余卷积注意 (MLRCA) 方法来改善特征表示.
- 将MLRCA集成到特征金字塔网络 (FPN) 中,创建用于多规模融合的ML-FPN.
- 实现了侧面意识边界定位 (SABL) 检测头,以准确定位缺陷.
主要成果:
- 基于MLRCA的模型实现了高性能指标:AP (83.4),AP50 (89.7),AP75 (85.6).
- 非常好地检测小 (AP_S: 71.3),中等 (AP_M: 89.9) 和大 (AP_L: 88.9) 的缺陷.
- 在区分类似的缺陷类别方面表现出卓越的能力.
结论:
- 拟议的MLRCA和ML-FPN方法为自动皮革缺陷检测提供了可行和有效的解决方案.
- 为提高皮革制造业的工业质量控制提供了新的见解.
- 该方法显示了检测微妙和轻微缺陷的巨大潜力.
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