A smart surface inspection system using faster RCNN in cloudedge computing environment Abstract   Automated surface inspection has become a hot topic with the rapid development of machine vision technologies. Traditional machine vision methods need experts to carefully craft image features for defect detection. This limits their applications to wider areas. The emerging convolutional neural networks (CNN) can automatically extract features and yield good results in many cases. However, the CNNbased image classification methods are more suitable for flat surface texture inspection. It is difficult to accurately locate small defects in geometrically complex products. Furthermore, the computational power required in CNN algorithms is usually high and it is not efficient to be implemented on embedded hardware. To solve these problems, a smart surface inspection system is proposed using faster RCNN algorithm in the cloudedge computing environment. The faster RCNN as a CNNbased object detection method can efficiently identify defects in complex product images and the cloudedge computing framework can provide fast computation speed and evolving algorithm models. A real industrial case study is presented to illustrate the effectiveness of the proposed method. The results show that the proposed method can provide high detection accuracy within a short time.

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.