Object classification with aggregating multiple spatial views using a machine-learning approach

[1]C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” 2016 IEEE conference on computer vision and pattern recognition, 2016.Search in Google Scholar

[2]C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, “Inception-v4, inception-resnet and the impact of residual connections on learning,” 2017 AAAI conference on artificial intelligence, 2017.Search in Google Scholar

[3]K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” 2016 IEEE conference on computer vision and pattern recognition, 2016.Search in Google Scholar

[4]K. He and J. Sun, “Convolutional neural networks at constrained time cost,” 2015 IEEE conference on computer vision and pattern recognition, 2015.Search in Google Scholar

[5]J. Redmon, S. Divvala, G. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” 2016 IEEE conference on computer vision and pattern recognition, 2016.Search in Google Scholar

[6]J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” 2015 IEEE conference on computer vision and pattern recognition, 2015.Search in Google Scholar

[7]R. Hu, M. Rohrbach, and T. Darrell, “Segmentation from natural language expressions,” 2016 Computer Vision–ECCV 2016: 14th European Conference, 2016.Search in Google Scholar

[8]R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” 2014 IEEE conference on computer vision and pattern recognition, 2014.Search in Google Scholar

[9]R. Girshick, “Fast r-cnn,” 2015 IEEE international conference on computer vision, 2015.Search in Google Scholar

[10]S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks”. Advances in neural information processing systems, vol. 28, 2015.Search in Google Scholar

[11]K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” 2017 IEEE international conference on computer vision, 2017.Search in Google Scholar

[12]PhoXi 3D Scanner S. Available online: https://www.photoneo.com/products/phoxi-scan-s/Search in Google Scholar

[13]S. Ahn, OpenGL Projection Matrix. Available online: http://www.songho.ca/opengl/gl_projectionmatrix.htmlSearch in Google Scholar

[14]A. Tharwat, “Classification assessment methods,” Applied computing and informatics, vol. 17, no. 1, pp. 168-192, 2020.Search in Google Scholar

[15]W. Wu, Z. Qi, and L. Fuxin, “Pointconv: Deep convolutional networks on 3d point clouds,” IEEE/CVF Conference on computer vision and pattern recognition, 2019.Search in Google Scholar

[16]A. Bochkovskiy, C. Y. Wang, and H. Y. M. Liao, “Yolov4: Optimal speed and accuracy of object detection, “ arXiv preprint arXiv:2004.10934, 2020.Search in Google Scholar

[17]H. Wang, S. Dong, S. Shi, A. Li, J. Li, Z. Li, and L. Wang, “Cagroup3d: Class-aware grouping for 3d object detection on point clouds,“ Advances in neural information processing systems, vol. 35, 2022.Search in Google Scholar

[18]L. D. Hanh and K. T. G. Hieu, “3D matching by combining CAD model and computer vision for autonomous bin picking,“ International Journal on Interactive Design and Manufacturing (IJIDeM), Vol. 15, pp. 239-247, 2021.Search in Google Scholar

[19]J. Wang, L. Song, Z. Li, H. Sun, J. Sun, and N. Zheng, “Endto-end object detection with fully convolutional network,“, 2021 IEEE/CVF conference on computer vision and pattern recognition, 2021.Search in Google Scholar

[20]X. Xu, M. Zhao, P. Shi, R. Ren, X. He, X. Wei, and H. Yang, “Crack detection and comparison study based on faster R-CNN and mask R-CNN,“ Sensors, vol. 22, no. 3 pp. 1215, 2022.Search in Google Scholar