A DeepLearningbased 3D Defect Quantitative Inspection System in CC Products Surface To create an intelligent surface region of interests (ROI) 3D quantitative inspection strategy a reality in the continuous casting (CC) production line, an improved 3D laser image scanning system (3DLDS) was established based on binocular imaging and deeplearning techniques. In 3DLDS, firstly, to meet the requirements of the industrial application, the CCD laser image scanning method was optimized in hightemperature experiments and secondly, we proposed a novel region proposal method based on 3D ROI initial depth location for effectively suppressing redundant candidate bounding boxes generated by pseudodefects in a realtime inspection process. Thirdly, a novel twostep defects inspection strategy was presented by devising a fusion deep CNN model which combined fully connected networks (for defects classificationrecognition) and fully convolutional networks (for defects delineation). The 3DLDSxe2x80x99 dichotomous inspection method of defects classification and delineation processes are helpful in understanding and addressing challenges for defects inspection in CC product surfaces. The applicability of the presented methods is mainly tied to the surface quality inspection for slab, strip and billet products.

A Hierarchical Feature Fusionbased Method for Defect Recognition with a Small Sample As one of the breakthroughs in modern manufacturing, deep learning (DL) performs largescale network architectures and achieves some outstanding performances in visionbased defect recognition. However, most of these largescale networks require a large sample for training, and a small sample might cause the networks overfitting and collapse. Since the defect often occurs with a low probability, it is costly to collect largescale samples. To overcome this problem, a hierarchical feature fusionbased method is introduced for defect recognition with a small sample. The proposed method divides a pretrained VGG16 network into different blocks, and learns the hierarchical features from the low and high level blocks. The results are better than the other methods. This result manifests the proposed method suits problem, and the defect recognition could be deployed earlier with the proposed method.