论文标题
有效的深度学习方法，用于识别有缺陷的铸造产品
Efficient Deep Learning Methods for Identification of Defective Casting Products
论文作者
论文摘要
最近，在任何大规模制造业中，质量检查都变得至关重要。为了减少人为错误，必须使用高效且低的计算AI算法来识别这种有缺陷的产品已成为必须进行的。在本文中，我们使用模型尺寸，性能和CPU潜伏期在检测有缺陷的铸造产品中比较并对比了各种预训练和定制的体系结构。我们的结果表明，自定义体系结构比预先训练的移动体系结构高效。此外，自定义型号的执行速度比MobilenetV2和Nasnet等轻型模型快6至9倍。训练参数的数量和自定义体系结构的模型大小明显低（分别〜386次，〜119次），比MobilenetV2和Nasnet等最佳性能模型。还对自定义体系结构进行了增强实验，以使模型更强大和可推广。我们的工作阐明了这些定制构建的架构在边缘和物联网设备上部署的效率，而转移学习模型可能并不总是理想的。相反，它们应该特定于数据集和当前的分类问题。
Quality inspection has become crucial in any large-scale manufacturing industry recently. In order to reduce human error, it has become imperative to use efficient and low computational AI algorithms to identify such defective products. In this paper, we have compared and contrasted various pre-trained and custom-built architectures using model size, performance and CPU latency in the detection of defective casting products. Our results show that custom architectures are efficient than pre-trained mobile architectures. Moreover, custom models perform 6 to 9 times faster than lightweight models such as MobileNetV2 and NasNet. The number of training parameters and the model size of the custom architectures is significantly lower (~386 times & ~119 times respectively) than the best performing models such as MobileNetV2 and NasNet. Augmentation experimentations have also been carried out on the custom architectures to make the models more robust and generalizable. Our work sheds light on the efficiency of these custom-built architectures for deployment on Edge and IoT devices and that transfer learning models may not always be ideal. Instead, they should be specific to the kind of dataset and the classification problem at hand.
