References
- Bochkovskiy, A., C.-Y. Wang, and H.-Y. M. Liao. 2020. YOLOv4: Optimal speed and accuracy of object detection. arXiv Preprint arXiv 2004.10934.
- Chandra, S., S. Tsogkas, and I. Kokkinos. 2015. Accurate human-limb segmentation in RGB-D images for intelligent mobility assistance robots. International Conference on Computer Vision Workshop: 436-42. doi: https://doi.org/10.1109/ICCVW.2015.64.
- Duan, K., S. Bai, L.-X. Xie, H.-G. Qi, Q.-M. Huang, and Q. Tian. 2019. Centernet: Keypoint triplets for object detection. International Conference on Computer Vision: 6568-77. doi: https://doi.org/10.1109/ICCV.2019.00667.
- Dunlop, D. D., S. L. Hughes, and L. M. Manheim. 1997. Disability in activities of daily living: Patterns of change and a hierarchy of disability. American Journal of Public Health 87 (3):378–83. doi:https://doi.org/10.2105/AJPH.87.3.378.
- Fang, G., N. M. Kwok, and G. Dissanayake. 2013. Skin colour detection using the statistical decision theory. Advanced Materials Research 694-697:1891–95. doi:https://doi.org/10.4028/scientific.net/AMR.694-697.1891.
- Fotouhi, M., M. H. Rohban, and S. Kasaei. 2009. Skin detection using contourlet-based texture analysis. Fourth International Conference on Digital Telecommunications: 59-64. doi: https://doi.org/10.1109/ICDT.2009.18.
- Girshick, R. 2015. Fast R-CNN. International Conference on Computer Vision: 1440-48. doi: https://doi.org/10.1109/ICCV.2015.169.
- Girshick, R., J. Donahue, T. Darrell, and J. Malik. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. Computer Vision and Pattern Recognition 580–87. doi:https://doi.org/10.1109/CVPR.2014.81.
- He, K.-M., X.-Y. Zhang, S.-Q. Ren, and J. Sun. 2015a. Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. International Conference on Computer Vision: 1026–34. doi: https://doi.org/10.1109/ICCV.2015.123.
- He, K.-M., X.-Y. Zhang, S.-Q. Ren, and J. Sun. 2015b. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 37 (9):1904–16. doi:https://doi.org/10.1109/TPAMI.2015.2389824.
- He, K.-M., X.-Y. Zhang, S.-Q. Ren, and J. Sun. 2016. Deep residual learning for image recognition. Computer Vision and Pattern Recognition 770–78. doi:https://doi.org/10.1109/CVPR.2016.90.
- Hussain, N., M. A. Khan, M. Sharif, S. A. Khan, A. A. Albesher, T. Saba, and A. Armaghan. 2020. A deep neural network and classical features based scheme for objects recognition: An application for machine inspection. Multimedia Tools and Applications 1-23. doi:https://doi.org/10.1007/s11042-020-08852-3.
- Ioffe, S., and C. Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. International Conference on Machine Learning 1:448–56.
- Kawulok, M., J. Kawulok, and J. Nalepa. 2014. Spatial-based skin detection using discriminative skin-presence features. Pattern Recognition Letters 41:3–13. doi:https://doi.org/10.1016/j.patrec.2013.08.028.
- Khan, M. A., K. Muhammad, M. Sharif, T. Akram, and V. H. C. D. Albuquerque. 2021c. Multi-class skin lesion detection and classification via teledermatology. IEEE Journal of Biomedical and Health Informatics. doi:https://doi.org/10.1109/JBHI.2021.3067789.
- Khan, M. A., M. S. Sarfraz, M. Alhaisoni, A. A. Albesher, S. Wang, and I. Ashraf. 2020. StomachNet: Optimal deep learning features fusion for stomach abnormalities classification. IEEE Access 8:197969–81. doi:https://doi.org/10.1109/ACCESS.2020.3034217.
- Khan, M. A., M. Sharif, T. Akram, R. Damaseviciu, and R. Maskeliunas. 2021d. Skin lesion segmentation and multiclass classification using deep learning features and improved moth flame optimization. Diagnostics 11 (5):1–26. doi:https://doi.org/10.3390/diagnostics11050811.
- Khan, M. A., N. Hussain, A. Majid, M. Alhaisoni, S. A. C. Bukhari, S. Kadry, Y. Nam, and Y.-D. Zhang. 2021b. Classification of positive COVID-19 CT scans using deep learning. Computers, Materials & Continua 66 (3):2923–38. doi:https://doi.org/10.32604/cmc.2021.013191.
- Khan, M. A., T. Akram, Y.-D. Zhang, and M. Sharif. 2021a. Attributes based skin lesion detection and recognition: A mask RCNN and transfer learning-based deep learning framework. Pattern Recognition Letters 143:58–66. doi:https://doi.org/10.1016/j.patrec.2020.12.015.
- Khan, M. A., Y.-D. Zhang, and M. Sharif. 2021. Pixels to classes: Intelligent learning framework for multiclass skin lesion localization and classification. Computers & Electrical Engineering 90:106956. doi:https://doi.org/10.1016/j.compeleceng.2020.106956.
- Law, H., and J. Deng. 2018. Cornernet: Detecting objects as paired keypoints. European Conference on Computer Vision 11218:765–81. doi:https://doi.org/10.1007/978-3-030-01264-9_45.
- LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. Nature 521 (7553):436–44. doi:https://doi.org/10.1038/nature14539.
- Lin, T.-Y., P. Dollár, R. Girshick, K.-M. He, B. Hariharan, and S. Belongie. 2017. Feature pyramid networks for object detection. Computer Vision and Pattern Recognition 936–44. doi:https://doi.org/10.1109/CVPR.2017.106.
- Liu, S., L. Qi, H.-F. Qin, J.-P. Shi, and J.-Y. Jia. 2018. Path aggregation network for instance segmentation. Computer Vision and Pattern Recognition 8759–68. doi:https://doi.org/10.1109/CVPR.2018.00913.
- Liu, W., D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg. 2016. SSD: Single shot multibox detector. European Conference on Computer Vision 9905:21–37. doi:https://doi.org/10.1007/978-3-319-46448-0_2.
- Misra, D. 2019. Mish: A self regularized non-monotonic neural activation function. arXiv Preprint arXiv 1908.08681.
- Nadian, A., and A. Talebpour. 2011. Pixel-based skin detection using sinc function. IEEE Symposium on Computers & Informatics. doi: https://doi.org/10.1109/ISCI.2011.5958934.
- Newell, A., K.-Y. Yang, and J. Deng. 2016. Stacked hourglass networks for human pose estimation. European Conference on Computer Vision 9912:483–99. doi:https://doi.org/10.1007/978-3-319-46484-8_29.
- Pattnaik, G., V. K. Shrivastava, and K. Parvathi. 2020. Transfer learning-based framework for classification of pest in tomato plants. Applied Artificial Intelligence 34 (13):981–93. doi:https://doi.org/10.1080/08839514.2020.1792034.
- Rashid, M., M. A. Khan, M. Alhaisoni, S.-H. Wang, S. R. Naqvi, A. Rehman, and T. Saba. 2020. A sustainable deep learning framework for object recognition using multi-layers deep features fusion and selection. Sustainability 12 (12):5037. doi:https://doi.org/10.3390/su12125037.
- Rashid, M., M. A. Khan, M. Sharif, M. Raza, M. M. Sarfraz, and F. Afza. 2019. Object detection and classification: A joint selection and fusion strategy of deep convolutional neural network and SIFT point features. Multimedia Tools and Applications 78 (12):15751–77. doi:https://doi.org/10.1007/s11042-018-7031-0.
- Redmon, J., and A. Farhadi. 2018. YOLOv3: An incremental improvement. arXiv Preprint arXiv 1804.02767.
- Redmon, J., S. Divvala, R. Girshick, and A. Farhadi. 2016. You only look once: Unified, real-time object detection. Computer Vision and Pattern Recognition 2016:779–88. doi:https://doi.org/10.1109/CVPR.2016.91.
- Ren, S.-Q., K. He, R. Girshick, and J. Sun. 2015. Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 28: 91–99.
- Rezatofighi, H., N. Tsoi, J. Y. Gwak, A. Sadeghian, I. Reid, and S. Savarese. 2019. Generalized intersection over union: A metric and a loss for bounding box regression. Computer Vision and Pattern Recognition 658–66. doi:https://doi.org/10.1109/CVPR.2019.00075.
- Tan, W. R., C. S. Chan, P. Yogarajah, and J. Condell. 2012. A fusion approach for efficient human skin detection. IEEE Transactions on Industrial Informatics 8 (1):138–47. doi:https://doi.org/10.1109/TII.2011.2172451.
- Uijlings, J. R. R., K. E. A. Van De Sande, T. Gevers, and A. W. M. Smeulders. 2013. Selective search for object recognition. International Journal of Computer Vision 104 (2):154–71. doi:https://doi.org/10.1007/s11263-013-0620-5.
- Wang, C.-Y., H.-Y. M. Liao, Y.-H. Wu, P.-Y. Chen, J.-W. Hsieh, and I.-H. Yeh. 2020. CSPNet: A new backbone that can enhance learning capability of CNN. Computer Vision and Pattern Recognition Workshops 1571–80. doi:https://doi.org/10.1109/CVPRW50498.2020.00203.
- Werle, J., and K. Hauer. 2016. Design of a bath robot system – User definition and user requirements based on International Classification of Functioning, disability and health (ICF). IEEE International Symposium on Robot and Human Interactive Communication: 459-466. doi: https://doi.org/10.1109/ROMAN.2016.7745159.
- Wu, X.-W., D. Sahoo, and S. C. H. Hoi. 2020. Recent advances in deep learning for object detection. Neurocomputing 396:39–64. doi:https://doi.org/10.1016/j.neucom.2020.01.085.
- Zhang, S.-F., C. Chi, Y.-Q. Yao, Z. Lei, and S. Z. Li. 2020. Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. Computer Vision and Pattern Recognition 9756–65. doi:https://doi.org/10.1109/CVPR42600.2020.00978.
- Zheng, Z.-H., P. Wang, W. Liu, J.-Z. Li, R.-G. Ye, and D.-W. Ren. 2020. Distance-IOU loss: Faster and better learning for bounding box regression. Proceedings of the AAAI Conference on Artificial Intelligence 34 (7):12993–3000. doi:https://doi.org/10.1609/aaai.v34i07.6999.
- Zhu, J.-Q., and C.-H. Cai. 2011. Region growing based high brightness skin detection. 10th International Symposium on Signals, Circuits and Systems. doi: https://doi.org/10.1109/ISSCS.2011.5978652.
- Zlatintsi, A., A. C. Dometios, N. Kardaris, I. Rodomagoulakis, P. Koutras, X. Papageorgiou, P. Maragos, C. S. Tzafestas, P. Vartholomeos, K. Hauer, et al. 2020. I-Support: A robotic platform of an assistive bathing robot for the elderly population. Robotics and Autonomous Systems 103451. doi:https://doi.org/10.1016/j.robot.2020.103451.