YOT-Net：用于铜管肘部表面缺陷检测的 YOLOv3 结合三元组损失网络。

YOT-Net: YOLOv3 Combined Triplet Loss Network for Copper Elbow Surface Defect Detection.

机构信息

College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.

School of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang 524088, China.

出版信息

Sensors (Basel). 2021 Oct 31;21(21):7260. doi: 10.3390/s21217260.

DOI:10.3390/s21217260

PMID:34770569

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8586934/

Abstract

Copper elbows are an important product in industry. They are used to connect pipes for transferring gas, oil, and liquids. Defective copper elbows can lead to serious industrial accidents. In this paper, a novel model named YOT-Net (YOLOv3 combined triplet loss network) is proposed to automatically detect defective copper elbows. To increase the defect detection accuracy, triplet loss function is employed in YOT-Net. The triplet loss function is introduced into the loss module of YOT-Net, which utilizes image similarity to enhance feature extraction ability. The proposed method of YOT-Net shows outstanding performance in copper elbow surface defect detection.

摘要

铜弯头是工业中的重要产品。它们用于连接输送气体、油和液体的管道。有缺陷的铜弯头可能导致严重的工业事故。本文提出了一种名为 YOT-Net（YOLOv3 结合三重损失网络）的新型模型，用于自动检测有缺陷的铜弯头。为了提高缺陷检测的准确性，在 YOT-Net 中采用了三重损失函数。三重损失函数被引入到 YOT-Net 的损失模块中，利用图像相似性来增强特征提取能力。所提出的 YOT-Net 方法在铜弯头表面缺陷检测中表现出优异的性能。