Araştırma Makalesi
BibTex RIS Kaynak Göster

Sectoral Application Analysis of Studies Made with Deep Learning Models

Yıl 2021, Cilt: 17 Sayı: 2, 126 - 140, 31.12.2021

Merve Yılmaz Hasan Şahin Aytaç Yıldız

Öz

Derin öğrenme, yapay sinir ağları algoritmalarını kullanarak çok katmanlı mimarilerde çok boyutlu veriler ile çalışma imkânı sağlayan, makine öğrenmesi alanının bir alt dalıdır. Derin öğrenme metotları sayesinde doğal dil işleme, görüntü işleme, görsel nesne tespiti, ilaç keşfi, vb. alanlarda ciddi bir şekilde başarım oranı artmıştır. Derin öğrenme, geri yayılım algoritmasını kullanıp çok boyutlu veri setlerinin karmaşık yapısını keşfederek insan düzeyine yakın görüntü sınıflandırması, insan düzeyinde konuşma tanıma, metin okuma ve seslendirme gibi konularda araştırmacılara kolaylıklar sağlamaktadır. Bu özelliklerinden dolayı derin öğrenme ve görüntü işleme yöntemleri günümüzde birçok alanda birçok problemin çözümünde hızlı bir şekilde kullanılmaya başlanmıştır. Bu çalışmada ilk olarak yapay zekâ ve derin öğrenmeye ait özet bilgiler verilmiştir. Daha sonra, derin öğrenme yöntemleri kullanılarak yapılan çalışmalar incelenerek derin öğrenmenin hangi alana nasıl uygulandığına dair somut örnekler verilmiştir. Çalışmanın son kısmında, incelenen makalelerin amaçları, kullandıkları yöntemler, literatüre olan katkıları ve elde ettikleri sonuçları içeren özet bir tablo sunularak araştırmacıların yapacakları çalışmalarda kullanacakları yöntemlere ilişkin ön bilgiler elde etmeleri sağlanmıştır.

Anahtar Kelimeler

Artificial intelligence Deep learning Image processing Object detection

Kaynakça

  • Balbozan, F.İ. (2011). Kameralı lazer tarama sistemi ile nesne sınıflandırması ve uygulamaları (Doctoral dissertation, DEÜ Fen Bilimleri Enstitüsü), DEÜ Fen Bilimleri Enstitüsü.
  • Brunetti, A., Buongiorno, D., Trotta, G. F., & Bevilacqua, V. (2018). Computer vision and deep learning techniques for pedestrian detection and tracking: A survey. Neurocomputing, 300, 17-33.
  • Carrell, S., & Atapour-Abarghouei, A. (2021). Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking. arXiv preprint arXiv:2109.02119.
  • Chen, S. H., Wang, C. W., Tai, I. H., Weng, K. P., Chen, Y. H., & Hsieh, K. S. (2021). Modified YOLOv4-DenseNet Algorithm for Detection of Ventricular Septal Defects in Ultrasound Images. International Journal Of Interactive Multimedia And Artificial Intelligence, 1-8.
  • Gupta, A., Manda, V. K., & Seraphim, B. I. (2021). Lung Cancer Detection Using Image Processing and Convolutional Neural Network. Annals of the Romanian Society for Cell Biology, 3044-3048.
  • Hacıefendioğlu, K., Başağa, H. B., & Demir, G. (2021). Automatic detection of earthquake-induced ground failure effects through Faster R-CNN deep learning-based object detection using satellite images. Natural Hazards, 105(1), 383-403.
  • Han, F., Yao, J., Zhu, H., & Wang, C. (2020). Underwater image processing and object detection based on deep CNN method. Journal of Sensors, 2020.
  • Hoang, N. D. (2020). Image Processing-Based Spall Object Detection Using Gabor Filter, Texture Analysis, and Adaptive Moment Estimation (Adam) Optimized Logistic Regression Models. Advances in Civil Engineering, 2020.
  • Hu, Y., Guo, Y., Wang, Y., Yu, J., Li, J., Zhou, S., & Chang, C. (2019). Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model. Medical physics, 46(1), 215-228.
  • Humm, B. G. (2016). Applied Artificial Intelligence An Engineering Approach.
  • Jiang, Q., Tan, D., Li, Y., Ji, S., Cai, C., & Zheng, Q. (2020). Object detection and classification of metal polishing shaft surface defects based on convolutional neural network deep learning. Applied Sciences, 10(1), 87.
  • Jin, X., Che, J., & Chen, Y. (2021). Weed Identification Using Deep Learning and Image Processing in Vegetable Plantation. IEEE Access, 9, 10940-10950.
  • Kako, S. I., Morita, S., & Taneda, T. (2020). Estimation of plastic marine debris volumes on beaches using unmanned aerial vehicles and image processing based on deep learning. Marine Pollution Bulletin, 155, 111127.
  • Kesav, N., & Jibukumar, M. G. (2021). Efficient and low complex architecture for detection and classification of Brain Tumor using RCNN with Two Channel CNN. Journal of King Saud University-Computer and Information Sciences.
  • Khowaja, A., & Nadir, D. (2019, December). Automatic fabric fault detection using image processing. In 2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS) (pp. 1-5). IEEE.
  • Kim, K. H., Koo, H. W., Lee, B. J., Yoon, S. W., & Sohn, M. J. (2021). Cerebral hemorrhage detection and localization with medical imaging for cerebrovascular disease diagnosis and treatment using explainable deep learning. Journal of the Korean Physical Society, 1-7.
  • Kiyak, E., & Unal, G. (2021). Small aircraft detection using deep learning. Aircraft Engineering and Aerospace Technology.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
  • Lee, W. C., Zhang, J. Y., & Wei, C. C. (2021). Using an LCD Monitor and a Robotic Arm to Quickly Establish Image Datasets for Object Detection. IEEE Access, 131006- 131019.
  • Liu, Y., Hou, M., Li, A., Dong, Y., Xie, L., & Ji, Y. (2020). Automatic detection of timber-cracks in wooden architectural heritage using YOLOv3 algorithm. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 1471-1476.
  • Muñoz-Villamizar, A., Rafavy, C. Y., & Casey, J. (2020). Machine learning and optimization-based modeling for asset management: a case study. International Journal of Productivity and Performance Management.
  • Murali, K., Krishna, V., Krishna, V., & Kumari, B. (2020). Application of deep learning and image processing analysis of photographs for amblyopia screening. Indian Journal of Ophthalmology, 68(7), 1407.
  • Ongsulee, P. (2017, November). Artificial intelligence, machine learning and deep learning. In 2017 15th International Conference on ICT and Knowledge Engineering (ICT&KE) (pp. 1-6). IEEE.
  • Provost, F., & Kohavi, R. (1998). Guest editors' introduction: On applied research in machine learning. Machine learning, 30(2), 127-132.
  • Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., & McCool, C. (2016). Deepfruits: A fruit detection system using deep neural networks. sensors, 16(8), 1222.
  • Saii, M., & Kraitem, Z. (2017). Automatic brain tumor detection in MRI using image processing techniques. Biomedical statistics and informatics, 2(2), 73-76.
  • Sekrecka, A., Kedzierski, M., & Wierzbicki, D. (2019). Pre-processing of panchromatic images to improve object detection in pansharpened images. Sensors, 19(23), 5146.
  • Serte, S., & Demirel, H. (2019). Gabor wavelet-based deep learning for skin lesion classification. Computers in biology and medicine, 113, 103423.
  • Sipetas, C., Keklikoglou, A., & Gonzales, E. J. (2020). Estimation of left behind subway passengers through archived data and video image processing. Transportation Research Part C: Emerging Technologies, 118, 102727.
  • Tao, T., Dong, D., Huang, S., & Chen, W. (2020). Gap detection of switch machines in complex environment based on object detection and image processing. Journal of Transportation Engineering, Part A: Systems, 146(8), 04020083.
  • Tümen, V., & Ergen, B. (2020). Intersections and crosswalk detection using deep learning and image processing techniques. Physica A: Statistical Mechanics and its Applications, 543, 123510.
  • Vandaele, R., Nervo, G. A., & Gevaert, O. (2020). Topological image modification for object detection and topological image processing of skin lesions. Scientific Reports, 10(1), 1-15.
  • Wainberg, M., Merico, D., Delong, A., & Frey, B. J. (2018). Deep learning in biomedicine. Nature biotechnology, 36(9), 829-838.
  • Wang, L., Lin, Z. Q., & Wong, A. (2020). Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific Reports, 10(1), 1-12.
  • Wei, X., Jiang, S., Li, Y., Li, C., Jia, L., & Li, Y. (2019). Defect detection of pantograph slide based on deep learning and image processing technology. IEEE Transactions on Intelligent Transportation Systems, 21(3), 947-958.
  • Zhang, H., Sfarra, S., Genest, M., Ibarra-Castanedo, C., Duan, Y., Fernandes, H., ... & Maldague, X. P. (2018). A comparative study of enhanced infrared image processing for foreign object detection in lightweight composite honeycomb structures. International Journal of Thermophysics, 39(12), 1-10.
  • Zhang, Y., Song, C., & Zhang, D. (2020). Deep learning-based object detection improvement for tomato disease. IEEE Access, 8, 56607-56614.
  • Zhao, Z. Q., Zheng, P., Xu, S. T., & Wu, X. (2019). Object detection with deep learning: A review. IEEE transactions on neural networks and learning systems, 30(11), 3212-3232.

Yıl 2021, Cilt: 17 Sayı: 2, 126 - 140, 31.12.2021

Merve Yılmaz Hasan Şahin Aytaç Yıldız

Öz

Kaynakça

  • Balbozan, F.İ. (2011). Kameralı lazer tarama sistemi ile nesne sınıflandırması ve uygulamaları (Doctoral dissertation, DEÜ Fen Bilimleri Enstitüsü), DEÜ Fen Bilimleri Enstitüsü.
  • Brunetti, A., Buongiorno, D., Trotta, G. F., & Bevilacqua, V. (2018). Computer vision and deep learning techniques for pedestrian detection and tracking: A survey. Neurocomputing, 300, 17-33.
  • Carrell, S., & Atapour-Abarghouei, A. (2021). Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking. arXiv preprint arXiv:2109.02119.
  • Chen, S. H., Wang, C. W., Tai, I. H., Weng, K. P., Chen, Y. H., & Hsieh, K. S. (2021). Modified YOLOv4-DenseNet Algorithm for Detection of Ventricular Septal Defects in Ultrasound Images. International Journal Of Interactive Multimedia And Artificial Intelligence, 1-8.
  • Gupta, A., Manda, V. K., & Seraphim, B. I. (2021). Lung Cancer Detection Using Image Processing and Convolutional Neural Network. Annals of the Romanian Society for Cell Biology, 3044-3048.
  • Hacıefendioğlu, K., Başağa, H. B., & Demir, G. (2021). Automatic detection of earthquake-induced ground failure effects through Faster R-CNN deep learning-based object detection using satellite images. Natural Hazards, 105(1), 383-403.
  • Han, F., Yao, J., Zhu, H., & Wang, C. (2020). Underwater image processing and object detection based on deep CNN method. Journal of Sensors, 2020.
  • Hoang, N. D. (2020). Image Processing-Based Spall Object Detection Using Gabor Filter, Texture Analysis, and Adaptive Moment Estimation (Adam) Optimized Logistic Regression Models. Advances in Civil Engineering, 2020.
  • Hu, Y., Guo, Y., Wang, Y., Yu, J., Li, J., Zhou, S., & Chang, C. (2019). Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model. Medical physics, 46(1), 215-228.
  • Humm, B. G. (2016). Applied Artificial Intelligence An Engineering Approach.
  • Jiang, Q., Tan, D., Li, Y., Ji, S., Cai, C., & Zheng, Q. (2020). Object detection and classification of metal polishing shaft surface defects based on convolutional neural network deep learning. Applied Sciences, 10(1), 87.
  • Jin, X., Che, J., & Chen, Y. (2021). Weed Identification Using Deep Learning and Image Processing in Vegetable Plantation. IEEE Access, 9, 10940-10950.
  • Kako, S. I., Morita, S., & Taneda, T. (2020). Estimation of plastic marine debris volumes on beaches using unmanned aerial vehicles and image processing based on deep learning. Marine Pollution Bulletin, 155, 111127.
  • Kesav, N., & Jibukumar, M. G. (2021). Efficient and low complex architecture for detection and classification of Brain Tumor using RCNN with Two Channel CNN. Journal of King Saud University-Computer and Information Sciences.
  • Khowaja, A., & Nadir, D. (2019, December). Automatic fabric fault detection using image processing. In 2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS) (pp. 1-5). IEEE.
  • Kim, K. H., Koo, H. W., Lee, B. J., Yoon, S. W., & Sohn, M. J. (2021). Cerebral hemorrhage detection and localization with medical imaging for cerebrovascular disease diagnosis and treatment using explainable deep learning. Journal of the Korean Physical Society, 1-7.
  • Kiyak, E., & Unal, G. (2021). Small aircraft detection using deep learning. Aircraft Engineering and Aerospace Technology.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
  • Lee, W. C., Zhang, J. Y., & Wei, C. C. (2021). Using an LCD Monitor and a Robotic Arm to Quickly Establish Image Datasets for Object Detection. IEEE Access, 131006- 131019.
  • Liu, Y., Hou, M., Li, A., Dong, Y., Xie, L., & Ji, Y. (2020). Automatic detection of timber-cracks in wooden architectural heritage using YOLOv3 algorithm. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 1471-1476.
  • Muñoz-Villamizar, A., Rafavy, C. Y., & Casey, J. (2020). Machine learning and optimization-based modeling for asset management: a case study. International Journal of Productivity and Performance Management.
  • Murali, K., Krishna, V., Krishna, V., & Kumari, B. (2020). Application of deep learning and image processing analysis of photographs for amblyopia screening. Indian Journal of Ophthalmology, 68(7), 1407.
  • Ongsulee, P. (2017, November). Artificial intelligence, machine learning and deep learning. In 2017 15th International Conference on ICT and Knowledge Engineering (ICT&KE) (pp. 1-6). IEEE.
  • Provost, F., & Kohavi, R. (1998). Guest editors' introduction: On applied research in machine learning. Machine learning, 30(2), 127-132.
  • Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., & McCool, C. (2016). Deepfruits: A fruit detection system using deep neural networks. sensors, 16(8), 1222.
  • Saii, M., & Kraitem, Z. (2017). Automatic brain tumor detection in MRI using image processing techniques. Biomedical statistics and informatics, 2(2), 73-76.
  • Sekrecka, A., Kedzierski, M., & Wierzbicki, D. (2019). Pre-processing of panchromatic images to improve object detection in pansharpened images. Sensors, 19(23), 5146.
  • Serte, S., & Demirel, H. (2019). Gabor wavelet-based deep learning for skin lesion classification. Computers in biology and medicine, 113, 103423.
  • Sipetas, C., Keklikoglou, A., & Gonzales, E. J. (2020). Estimation of left behind subway passengers through archived data and video image processing. Transportation Research Part C: Emerging Technologies, 118, 102727.
  • Tao, T., Dong, D., Huang, S., & Chen, W. (2020). Gap detection of switch machines in complex environment based on object detection and image processing. Journal of Transportation Engineering, Part A: Systems, 146(8), 04020083.
  • Tümen, V., & Ergen, B. (2020). Intersections and crosswalk detection using deep learning and image processing techniques. Physica A: Statistical Mechanics and its Applications, 543, 123510.
  • Vandaele, R., Nervo, G. A., & Gevaert, O. (2020). Topological image modification for object detection and topological image processing of skin lesions. Scientific Reports, 10(1), 1-15.
  • Wainberg, M., Merico, D., Delong, A., & Frey, B. J. (2018). Deep learning in biomedicine. Nature biotechnology, 36(9), 829-838.
  • Wang, L., Lin, Z. Q., & Wong, A. (2020). Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific Reports, 10(1), 1-12.
  • Wei, X., Jiang, S., Li, Y., Li, C., Jia, L., & Li, Y. (2019). Defect detection of pantograph slide based on deep learning and image processing technology. IEEE Transactions on Intelligent Transportation Systems, 21(3), 947-958.
  • Zhang, H., Sfarra, S., Genest, M., Ibarra-Castanedo, C., Duan, Y., Fernandes, H., ... & Maldague, X. P. (2018). A comparative study of enhanced infrared image processing for foreign object detection in lightweight composite honeycomb structures. International Journal of Thermophysics, 39(12), 1-10.
  • Zhang, Y., Song, C., & Zhang, D. (2020). Deep learning-based object detection improvement for tomato disease. IEEE Access, 8, 56607-56614.
  • Zhao, Z. Q., Zheng, P., Xu, S. T., & Wu, X. (2019). Object detection with deep learning: A review. IEEE transactions on neural networks and learning systems, 30(11), 3212-3232.
Toplam 38 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Merve Yılmaz 0000-0002-1236-355X

Hasan Şahin 0000-0002-8915-000X

Aytaç Yıldız 0000-0002-0729-633X

Yayımlanma Tarihi 31 Aralık 2021
Gönderilme Tarihi 6 Ekim 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 17 Sayı: 2

Kaynak Göster

APA Yılmaz, M., Şahin, H., & Yıldız, A. (2021). Sectoral Application Analysis of Studies Made with Deep Learning Models. Electronic Letters on Science and Engineering, 17(2), 126-140.
 Kapak Resmi İndir