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Abstract:
In order to fulfill customer''s requirement, companies have to guarantee the quality of delivered products, which can often be achieved only by manually inspection of the finished product. Since human-based defect inspection and classification are time-consuming and the results vary by individuals, automatic defect detection and classification has the potential to reduce the cost of quality assurance significantly. In this talk, we will demonstrate how to utilize deep learning algorithms, i.e., Fully Convolutional Neural Network to build a general defect inspection and classification model. We will also share experiences on how to effectively collect labelling data, deal with imbalance data, and also how to optimize the model in terms of latency and throughput with TensorRT before deploy the model to the production line.
In order to fulfill customer''s requirement, companies have to guarantee the quality of delivered products, which can often be achieved only by manually inspection of the finished product. Since human-based defect inspection and classification are time-consuming and the results vary by individuals, automatic defect detection and classification has the potential to reduce the cost of quality assurance significantly. In this talk, we will demonstrate how to utilize deep learning algorithms, i.e., Fully Convolutional Neural Network to build a general defect inspection and classification model. We will also share experiences on how to effectively collect labelling data, deal with imbalance data, and also how to optimize the model in terms of latency and throughput with TensorRT before deploy the model to the production line.  Back
 
Topics:
AI Application Deployment and Inference, Industrial Inspection, IoT, Robotics & Drones, Robotics & Autonomous Machines
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8682
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