Developing a Resource-Constraint EdgeAI model for Surface Defect
  Detection
Atah Nuh Mih Hung Cao Asfia Kawnine Monica Wachowicz
Resource constraints have restricted several EdgeAI applications to machine
learning inference approaches, where models are trained on the cloud and
deployed to the edge device. This poses challenges such as bandwidth, latency,
and privacy associated with storing data off-site for model building. Training
on the edge device can overcome these challenges by eliminating the need to
transfer data to another device for storage and model development. On-device
training also provides robustness to data variations as models can be retrained
on newly acquired data to improve performance. We, therefore, propose a
lightweight EdgeAI architecture modified from Xception, for on-device training
in a resource-constraint edge environment. We evaluate our model on a PCB
defect detection task and compare its performance against existing lightweight
models - MobileNetV2, EfficientNetV2B0, and MobileViT-XXS. The results of our
experiment show that our model has a remarkable performance with a test
accuracy of 73.45% without pre-training. This is comparable to the test
accuracy of non-pre-trained MobileViT-XXS (75.40%) and much better than other
non-pre-trained models (MobileNetV2 - 50.05%, EfficientNetV2B0 - 54.30%). The
test accuracy of our model without pre-training is comparable to pre-trained
MobileNetV2 model - 75.45% and better than pre-trained EfficientNetV2B0 model -
58.10%. In terms of memory efficiency, our model performs better than
EfficientNetV2B0 and MobileViT-XXS. We find that the resource efficiency of
machine learning models does not solely depend on the number of parameters but
also depends on architectural considerations. Our method can be applied to
other resource-constraint applications while maintaining significant
performance.