## 6 Conclusions  
This paper proposes a novel method to improve the automated defect inspection on a hot rolled steel strip by introducing the multi-receptive field fusion-based network (MRFFN). Here, the Gaussian pyramid is applied to provide more meaningful information from the limited data. Subsequently, two lightweight multi-receptive field networks have been proposed to learn multilevel information. Besides, high learning rate factors are implemented for the shallower layers to accelerate the convergence of the training process and to prevent the gradient vanishing while training the deep networks. The experimental results based on the NEU dataset indicate that the proposed MRFFN only requires small training samples to achieve high accuracy compared with the state-of-the-art. Furthermore, the robustness of the proposed method has been evaluated on the disturbance defect dataset. It can be observed that the proposed method performs significantly better in comparison with the conventional CNN model against Gaussian white noise, salt and pepper noise, and motion blur. However, there are some limitations in this article. Firstly, the Gaussian pyramid takes several low pass filtering and downsampling processes for the raw images, which implies that the high-level images obtain relatively less information for the training model. Thus, the performance of the training model should be considered to assure that important features are retained in the high-level images. Additionally, the proposed MRFFN requires two deep networks that contain a massive training parameter. Hence, it may impede the efficiency of the inspection speed and require powerful hardware for the high computational load. Therefore, for the future, attention can be devoted to the following direction.First, the PCA can be adopted to eliminate the redundant information from the raw data. Then, a deformable feature extraction technique can be introduced to learn the multiscale features to acquire a more accurate description of image content. Conceptualization, K.-C.W. ; methodology, K.-C.W., W.-P.T. and S.-T.L. ; formal analysis, W.-P.T., S.-T.L. ; writing--original draft preparation, W.-P.T. and S.-T.L. ; writing--review and editing, C.-C.C., P.-C.H., M.-H.T., Y.-T.T. and S.-H.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the Ministry of Science and Technology, Taiwan (MOST 108-2221-E-158-003, MOST 109-2622-E-035-022, MOST 109-2221-E-035-001-MY2, and MOST 109-2221-E-035-065-MY2). Not applicable. +++ ==WARNING: Truncated because of repetitions==
[rightmargin=*]  
++  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Going Deeper with Convolutions. In Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 7-12 June 2015; pp. 1-9. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Review on Recent Advances in Vision-based Defect Recognition towards Industrial Intelligence. _J. Manuf. Syst._**2021**. online ahead of print. [CrossRef]
* [PERSON] (2008) [PERSON] A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques. _Electron. Lett. Comput. Vis. Image Anal._**2008**, \(7\), 1-22. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Automated leather defect inspection using statistical approach on image intensity. _J. Ambient. Intell. Humaniz. Comput._**2020**, _12_, 1-17. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] GLCM and ANN based Approach for Classification of Radiographics Weld Images. In Proceedings of the 2018 IEEE 13 th International Conference on Industrial and Information Systems (ICIIS), Rupangar, India, 1-2 December 2018. * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Distributed defect recognition on steel surfaces using an improved random forest algorithm with optimal multi-feature-set fusion. _Multimed.