Laser & Optoelectronics Progress, Volume. 57, Issue 10, 101501(2020)
Aluminum Surface-Defect Detection Based on Multi-Task Deep Learning
In industrial aluminum defect detection, sparse defect samples always lead to the training overfit and poor generalization. This study describes a defect detection model based on multi-task deep learning. Based on Faster RCNN, a multi-task deep network model is designed, including the aluminum area segmentation, defect multi-label classification, and defect target detection. Then the multi-task loss layer is designed, and the weights are balanced by using adaptive weights to solve the problem of uneven convergence in multi-task training. Experiment results show that with the support of a limited dataset, the proposed method can improve the accuracy of multi-label classification and defect target detection while maintaining the optimal mean intersection over union (MIoU) index of the segmentation task, compared to single-task learning. The method solves the problem of low detection accuracy caused by fewer samples of aluminum defect detection. For multi-tasking application scenarios, the model can simultaneously complete three tasks, while reducing the inference time and improving the detection efficiency.
Get Citation
Copy Citation Text
Xiaohai Shen, Zehao Li, Min Li, Xiaolong Xu, Xuewu Zhang. Aluminum Surface-Defect Detection Based on Multi-Task Deep Learning[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101501
Category: Machine Vision
Received: Jul. 15, 2019
Accepted: Oct. 18, 2019
Published Online: May. 8, 2020
The Author Email: Zhang Xuewu (lab_112@126.com)