EfficientDet for fabric defect detection based on edge computing

Shaojun Song,Mingyang Shi,Junfeng Jing,Yanqing Huang

doi:10.1177/15589250211008346

Shaojun Song, Mingyang Shi + Show 2 more

Open Access

https://doi.org/10.1177/15589250211008346

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Abstract

The productivity of textile industry is positively correlated with the efficiency of fabric defect detection. Traditional manual detection methods have gradually been replaced by deep learning algorithms based on cloud computing due to the low accuracy and high cost of manual methods. Nonetheless, these cloud computing-based methods are still suboptimal due to the data transmission latency between the end devices and the cloud. To facilitate defect detection with more efficiency, a low-latency, low power consumption, easy upgrade, and automatical visual inspection system with the help of edge computing are proposed in this work. Firstly, the method uses EfficientDet-D0 as the detection algorithm, integrating the advantages of lightweight and scalable and can suit the resource-constrained edge device. Secondly, we performed data augmentations on five fabric datasets and verified the adaptability of the model in different types of fabrics. Finally, we transplanted the trained model to the edge device NVIDIA Jetson TX2 and optimized the model with TensorRT to make it detection faster. The performance of the proposed method is evaluated in five fabric datasets. The detection speed is up to 22.7 frame per second (FPS) on the edge device Jetson TX2. Compared with the cloud-based method, the response time is reduced by 2.5 times, with the capability of real-time industrial defect detection.

Highlights

TEXTILES are widely used in our lives, such as clothes, towels, filter cloth, and in some select fields, such as aerospace, medical hygiene.[1,2] in the process of textile production, it is inevitable to produce defective fabrics
The main contributions of this work are listed as follows: 1. This paper proposes an automatic fabric defect detection system that combines EfficientDet and edge computing, and edge device is applied to fabric defect detection
To satisfy the requirements of the industrial automatic fabric defect detection system, we proposed an automated fabric defect detection method based on edge computing

Summary

Introduction

TEXTILES are widely used in our lives, such as clothes, towels, filter cloth, and in some select fields, such as aerospace, medical hygiene.[1,2] in the process of textile production, it is inevitable to produce defective fabrics. Most of the defects are caused by machine failures, defective yarns, and oil stains on sewing gadgets.[2] These unqualified textiles will damage the reputation of the company but even have a vital impact on the safety of certain products. The price of second-class textile fabrics is reduced by 45%–65% compared with first-class textile fabrics.[3] to boost profits and product competitiveness, fabric defect inspection has become an essential step. The automated fabric defect inspection system is desirable for quality control of the textile industry. College of Electronics and Information, Xi’an Polytechnic University, Xi’an, China

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