Summary of Developing a Resource-constraint Edgeai Model For Surface Defect Detection, by Atah Nuh Mih et al.
Developing a Resource-Constraint EdgeAI model for Surface Defect Detection
by Atah Nuh Mih, Hung Cao, Asfia Kawnine, Monica Wachowicz
First submitted to arxiv on: 4 Dec 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a lightweight EdgeAI architecture, modified from Xception, for on-device training in a resource-constrained edge environment. The proposed model is designed to overcome the challenges of bandwidth, latency, and privacy associated with storing data off-site for model building. On-device training provides robustness to data variations as models can be retrained on newly acquired data to improve performance. The authors evaluate their model on a PCB defect detection task and compare its performance against existing lightweight models – MobileNetV2, EfficientNetV2B0, and MobileViT-XXS. The results show that the proposed model has a remarkable performance with a test accuracy of 73.45% without pre-training, comparable to non-pre-trained MobileViT-XXS (75.40%) and better than other non-pre-trained models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to train machine learning models on devices like smartphones or smart home devices, instead of sending data to the cloud. This makes it faster and more private. The authors tested their method on detecting defects in printed circuit boards (PCBs) and found that it works well. They compared their results to other methods and showed that their approach is better. |
Keywords
* Artificial intelligence * Machine learning
