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.
翻译:最近,质量检查在任何大型制造业中都变得至关重要。为了减少人为错误,必须使用高效和低计算性AI算法来识别此类缺陷产品。在本文件中,我们比较和对比了使用模型大小、性能和显微性能检测有缺陷的铸造产品的各种预先培训和定制结构。我们的结果表明,定制结构比预先培训的移动结构效率高。此外,定制模型比诸如移动NetV2和NasNet等轻量级模型高出6至9倍。定制结构的培训参数和模型规模大大低于(分别为~386倍和~119倍)最优秀的模型,例如移动NetV2和NasNet。还在定制结构上进行了强化实验,以使模型更加可靠和普及。我们的工作揭示了这些定制建筑在Edge和IoT设备上部署的效率,转移学习模型可能并非始终理想。相反,它们应该具体用于数据集和分类问题手头。