A Light-Weight Defect Detection System for Edge Computing

Recently, many factories have utilized AI to help Automatic Optical Inspection (AOI) machines accurately detect defects. They usually deploy AI models on the clouds and submit the data to the clouds for inference. However, transmission delay increases the response time of the AI model. If AI can differentiate defects on the local edge devices, the production efficiency can be significantly improved. In this paper, we propose a light-weight defect detection system that utilizes pruning techniques to compress the model and can accurately detect defects at a faster speed. Besides, we compare the performance of pruned and unpruned models on Kneron KL520 AI dongle and NVIDIA Jetson Nano to verify the superior ability of pruning to accelerate inference. The accuracy of the pruned model in the proposed system can reach 97.7% on Kneron KL520 AI dongle. The inference speed is 28.2 frames per second, 1.6 times faster than the unpruned model. Also, compared to NVIDIA Jetson Nano, the inference speed on Kneron KL520 AI dongle is two times faster. This result shows the better performance of Kneron KL520 AI dongle than NVIDIA Jetson Nano on inference. In summary, the proposed system can significantly improve the efficiency of production lines and avoid the information security risks brought by cloud computing.