A gradient fusion-based image data augmentation method for reflective workpieces detection under small size datasets

Baori Zhang1, Haogang Cai1, Lingxiang Wen1
1Ji Hua Laboratory, Foshan 528200, Guangdong Province, China

Tóm tắt

Từ khóa


Tài liệu tham khảo

QibtiAE, R.: Convolutional neural network model in machine learning methods and computer vision for image recognition: a review. In: ICEBS 2018 (2018) https://doi.org/10.22587/jasr.2018.14.6.5

Hartwig, S., Ropinski, T.: Training object detectors on synthetic images containing reflecting materials (2019) arXiv:1904.00824

Astanin, S., Antonelli, D., Chiabert, P., et al.: Reflective workpiece detection and localization for flexible robotic cells. Robot. Comput. Integr. Manuf. 44, 190–198 (2017)

Rosati, G., Boschetti, G., Biondi, A., et al.: Real-time defect detection on highly reflective curved surfaces. Opt. Lasers Eng. 47(3–4), 379–384 (2009). https://doi.org/10.1016/j.optlaseng.2008.03.010

Yang, J., Gao, Y., Li, D., et al.: ROBI: A multi-view dataset for reflective objects in robotic bin-picking. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic. pp 9788–9795 (2021) arXiv:2105.04112

Lu, Q., Laligant, O., Fauvet, E., et al.: Entire reflective object surface structure understanding. In: Proceedings of the British Machine Vision Conference (BMVC), Swansea, United Kingdom (2015) https://doi.org/10.1016/j.patrec.2015.09.006

Yang, D., Jayawardena, S., Gould, S., et al.: Reflective features detection and hierarchical reflections separation in image sequences. In: 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–7 (2014) https://doi.org/10.1109/DICTA.2014.7008127

Park, D., Park, Y.H.: Identifying reflected images from object detector in indoor environment utilizing depth information. IEEE Robot. Autom. Lett. 6(2), 635–642 (2021). https://doi.org/10.1109/LRA.2020.3047796

Hestness, J., Narang, S., Ardalani, N., et al.: Deep Learning Scaling is Predictable, Empirically. arXiv e-prints (2017) arXiv.1712.00409

Aik, L.E., Hong, T.W., Junoh, A.K.: A new formula to determine the optimal dataset size for training neural networks. ARPN J. Eng. Appl. Sci. 14, 52–61 (2019)

Zhang, C., Cheng, J.: Image scoring: Patch based CNN model for small or medium dataset. In: 2017 3rd IEEE International Conference on Computer and Communications (ICCC). pp. 2055–2059 (2017) https://doi.org/10.1109/CompComm.2017.8322898

Jalali, A., Mallipeddi, R., Lee, M.: Sensitive deep convolutional neural network for face recognition at large standoffs with small dataset. Expert Syst. Appl. 87, 304–315 (2017). https://doi.org/10.1016/j.eswa.2017.06.025

Tan, W., Guo, H.: Data augmentation and CNN classification for automatic COVID-19 diagnosis from CT-scan images on small dataset. arXiv e-prints (2021) arXiv:2108.07148

AEmed, T., RAEman, C. R., Abid, M.: Rice grain disease identification using dual phase convolutional neural network-based system aimed at small dataset. arXiv e-prints (2020) arXiv:2004.09870

Zhao., W.: Research on the deep learning of the small sample data based on transfer learning. In: American Institute of Physics Conference Series American Institute of Physics Conference Series, 020018 (2017) https://doi.org/10.1063/1.4992835

Tripuraneni, N., Jordan, M. I., Jin, C.: On the theory of transfer learning: the importance of task diversity (2020) arXiv:2006.11650

Liang, H., Fu, W., & Yi, F.: A survey of recent advances in transfer learning. In: 2019 IEEE 19th International Conference on Communication Technology (ICCT) (2019) https://doi.org/10.1109/icct46805.2019.8947072

Gozes, O., Greenspan, H.: Deep feature learning from a hospital-scale chest X-ray dataset with application to TB detection on a small-scale dataset. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4076–4079 (2019) https://doi.org/10.1109/EMBC.2019.8856729

Girshick, R., DonAEue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) https://doi.org/10.1109/CVPR.2014.81

Chen, L.C., Papandreou, G., Kokkinos, I., et al.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018). https://doi.org/10.1109/TPAMI.2017.2699184

Gao, X., Guanghui, L.I., Tan, R., et al.: Using deep neural networks to predict the tensile property of ceramic matrix composites based on incomplete small dataset. In: 4th International Conference on Advanced Materials Research and Manufacturing Technology (2019) https://doi.org/10.1088/1757-899X/647/1/012004

Popovic, V., Seyid, K., Pignat, E., et al.: Multi-camera platform for panoramic real-time HDR video construction and rendering. J. Real Time Image Process. 12(4), 697–708 (2016). https://doi.org/10.1007/s11554-014-0444-8

Kao, C., Cheng, L.W., Chien, C.-Y., et al.: Robust brightness measurement and exposure control in real-time video recording. IEEE Trans. Instrum. Meas. 60(4), 1206–1216 (2011). https://doi.org/10.1109/TIM.2010.2087835

Zhang, B., Shi, Y., Cui, Y., et al.: A high-dynamic-range visual sensing method for feature extraction of welding pool based on adaptive image fusion. Int. J. Adv. Manuf. Technol. 117, 1675–1687 (2021). https://doi.org/10.1007/s00170-021-07812-x

Zhang, B., Shi, Y., Cui, Y., et al.: Prediction of keyhole TIG weld penetration based on high-dynamic range imaging. J. Manuf. Process. 63, 179–190 (2021). https://doi.org/10.1016/j.jmapro.2020.03.053

Sevcenco, I.S., Hampton, P.J., Agathoklis, P.: A wavelet based method for image reconstruction from gradient data with applications. Multidimens. Syst. Signal Process. 26(3), 717–737 (2013). https://doi.org/10.1007/s11045-013-0262-3

Paul, S., Sevcenco, I.S., Agathoklis, P.: Multi-exposure and multi-focus image fusion in gradient domain. J. Circuits. Syst. Comput. 25(10), 1650123 (2016). https://doi.org/10.1142/s0218126616501231

Zhang, X., Ye, P., Xiao, G.: VIFB: a visible and infrared benchmark. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2020) arXiv:2002.03322

Bavirisetti, D.P., Dhuli, R.: Fusion of infrared and visible sensor images based on anisotropic diffusion and karhunenloeve transform. IEEE Sens. J. 16(1), 203–209 (2016)

Zhou, Z., Dong, M., Xie, X., et al.: Fusion of infrared and visible images for night-vision context enhancement. Appl. Opt. 55(23), 6480–6490 (2016). https://doi.org/10.1364/AO.55.006480

Ma, J., Chen, C., Li, C., et al.: Infrared and visible image fusion via gradient transfer and total variation minimization. Inf. Fusion 31, 100–109 (2016). https://doi.org/10.1016/j.inffus.2016.02.001

Bavirisetti, D.P., Xiao, G., Zhao, J., et al.: Multi-scale guided image and video fusion: a fast and efficient approach. Circuits Syst. Signal Process. 38(12), 5576–5605 (2019). https://doi.org/10.1007/s00034-019-01131-z

Naidu, V.: Image fusion technique using multi-resolution singular value decomposition. Defence Sci. J. 61(5), 479–484 (2011). https://doi.org/10.14429/dsj.61.705

Zhou, B., Khosla, A., Lapedriza, A., et al.: Learning deep features for discriminative localization. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) https://doi.org/10.1109/cvpr.2016.319

Selvaraju, R.R., Cogswell, M., Das, A., et al.: Grad-CAM: visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vis. 128(2), 336–359 (2019). https://doi.org/10.1007/s11263-019-01228-7

Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the International Conference on Machine Learning. pp. 233–240 (2006) https://doi.org/10.1145/1143844.1143874

Powers, D.M.W.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation (2020) https://doi.org/10.48550/arXiv.2010.16061