Efficient Deep Learning Methods for Identification of Defective Casting Products

TL;DR

This paper evaluates various deep learning architectures for detecting defective casting products, demonstrating that custom models outperform pre-trained ones in speed, size, and efficiency, making them suitable for edge deployment.

Contribution

It introduces and compares custom deep learning architectures with pre-trained models, highlighting their superior efficiency and suitability for manufacturing defect detection.

Findings

Custom architectures are 6-9 times faster than lightweight models.

Custom models have significantly fewer parameters and smaller size.

Transfer learning models may not be optimal for specific defect detection tasks.

Abstract

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.