This paper proposes a multi-task learning system for industrial defect detection, focusing on surface scratches on titanium gaskets as the detection target. Our model is built using a deep convolutional neural network (CNN). As defects are infrequent in actual production lines, we adopt the Faster-RCNN object detection network architecture and integrate it with a multi-task learning system. A classification task model is introduced into the backbone network to filter out a significant number of defect-free images. This step reduces the computation and data transmission time of the subsequent target detection task, accelerating the detection process. Experimental results show that our proposed method reduces inference time by 37.6% and 42.46% in actual production lines with defect rates of 12% and 5%, respectively, while maintaining 96% of the original model's performance.

19^th IMEKO TC10 Conference "Measurement for Diagnostics, Optimisation and Control to Support Sustainability and Resilience"