基于轻量级卷积神经网络的带钢表面缺陷识别

带钢表面缺陷识别对促进带钢生产质量提升至关重要。然而传统的图像处理与识别方法存在精度不高、且容易受到光线等因素影响；而新兴的基于深度学习的算法，则存在模型参数量大且难以部署等问题，无法在实际生产中得到广泛应用。本文提出了一种轻量级部分深度混合可分离网络(PDMSNet)用于解决以上问题，由于其模型小以及浮点型运算(FLOPs)少更易于部署在资源受限的平台。采用标准的带钢表面缺陷数据集NEU-CLS的测试结果表明，与其他缺陷分类器相比，在带钢表面缺陷检测方面，本文所提出的模型性能更加优越。识别准确率达到了99.78%,而参数量只有0.17 M以及272 M FLOPs,在单一低端的GeForce MX250图形处理单元(GPU)识别一张图片平均时间为0.47 ms,可以满足工业现场实时检测的要求。

Steel surface defect recongnition based on a lightweight convolutional neural network

Recognition of steel surface defect is essential to promote the improvement of steel production quality. However, the traditional image processing and recognition methods have low accuracy and are easily affected by factors such as light. However, the emerging algorithms based on deep learning have problems such as large amount of model parameters and difficulty in deployment, which cannot be widely used in practical production. In this article, a lightweight partial depth mixture separable network ( PDMSNet) is proposed, which is small model size and floating-point operations (FLOPs) and is easy to deploy on resource-constrained platforms. The test results of the standard strip steel surface defect data set NEU-CLS show that the performance of the proposed model is better than other defect classifiers in strip steel surface defect detection. The recognition accuracy reaches 99. 78% , and the number of parameters is only 0. 17 M and 272 M FLOPs. The average time of an image recognition on a single low-end GeForce MX 250 GPU is 0. 47 ms, which can meet the requirements of real-time detection in industrial field.