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 Traditional manual detection methods have gradually been replaced by deep learning algorithms based on cloud computing due to the low accuracy and high cost of manual methods.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.

纺织工业的生产率与织物缺陷检测的效率呈正相关。由于人工方法的准确性低且成本高，传统的人工检测方法已逐渐被基于云计算的深度学习算法所取代。但是，由于最终设备和云之间的数据传输延迟，这些基于云计算的方法仍然不是最佳选择。为了提高缺陷检测效率，提出了一种低延迟，低功耗，易于升级，并借助边缘计算的自动外观检查系统。首先，该方法采用EfficientDet-D0作为检测算法，兼具轻巧和可扩展的优点，可以适应资源受限的边缘设备。第二，我们对五个面料数据集进行了数据扩充，并验证了该模型在不同类型面料中的适应性。最后，我们将训练后的模型移植到边缘设备NVIDIA Jetson TX2上，并使用TensorRT优化了模型，以使其检测速度更快。在五个织物数据集中评估了该方法的性能。边缘设备Jetson TX2上的检测速度高达每秒22.7帧（FPS）。与基于云的方法相比，响应时间减少了2.5倍，具有实时工业缺陷检测功能。在五个织物数据集中评估了该方法的性能。边缘设备Jetson TX2上的检测速度高达每秒22.7帧（FPS）。与基于云的方法相比，响应时间减少了2.5倍，具有实时工业缺陷检测的能力。在五个织物数据集中评估了该方法的性能。边缘设备Jetson TX2上的检测速度高达每秒22.7帧（FPS）。与基于云的方法相比，响应时间减少了2.5倍，具有实时工业缺陷检测功能。