Efficient Deep Learning Methods for Identification of Defective Casting Products
This work addresses quality inspection for manufacturing industries by proposing efficient models for edge deployment, though it is incremental as it compares existing methods on a specific dataset.
The paper tackled the problem of identifying defective casting products in manufacturing by comparing pre-trained and custom-built deep learning architectures, finding that custom models are 6 to 9 times faster and have significantly fewer parameters (e.g., ~386 times lower) than models like MobileNetV2 and NasNet.
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.