针对工业铝材缺陷检测中由缺陷样本稀疏带来的训练过拟合、泛化性能差等问题,提出一种基于多任务深度学习的铝材缺陷检测方法。先基于Faster RCNN设计一个包含铝材区域分割、缺陷多标签分类和缺陷目标检测的多任务深度网络模型;再设计多任务损失层,利用自适应权重对各项任务进行加权平衡,解决了多项任务训练中的收敛不均衡问题。实验结果表明,在有限的数据集支持下,相较于单任务学习,该方法能够在保持分割任务的均交并比(MIoU)指标最优的情况下,分别提高多标签分类和缺陷目标检测的准确率,解决了由铝材缺陷检测样本少引起的检测精度较低的问题。对于多任务应用场景,该模型能够同时完成三个任务,减少推断时间,提高检测效率。

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.