  One of the most pressing challenges prevalent in the steel manufacturing
industry is the identification of surface defects. Early identification of
casting defects can help boost performance, including streamlining production
processes. Though, deep learning models have helped bridge this gap and
automate most of these processes, there is a dire need to come up with
lightweight models that can be deployed easily with faster inference times.
This research proposes a lightweight architecture that is efficient in terms of
accuracy and inference time compared with sophisticated pre-trained CNN
architectures like MobileNet, Inception, and ResNet, including vision
transformers. Methodologies to minimize computational requirements such as
depth-wise separable convolution and global average pooling (GAP) layer,
including techniques that improve architectural efficiencies and augmentations,
have been experimented. Our results indicate that a custom model of 590K
parameters with depth-wise separable convolutions outperformed pretrained
architectures such as Resnet and Vision transformers in terms of accuracy
(81.87%) and comfortably outdid architectures such as Resnet, Inception, and
Vision transformers in terms of faster inference times (12 ms). Blurpool fared
outperformed other techniques, with an accuracy of 83.98%. Augmentations had a
paradoxical effect on the model performance. No direct correlation between
depth-wise and 3x3 convolutions on inference time, they, however, they played a
direct role in improving model efficiency by enabling the networks to go deeper
and by decreasing the number of trainable parameters. Our work sheds light on
the fact that custom networks with efficient architectures and faster inference
times can be built without the need of relying on pre-trained architectures.
