DIFFERENT APPLICATION AREAS OF OBJECT DETECTION WITH DEEP LEARNING

Sevcan Turan; Bahar Milani; Feyzullah Temurtaş

doi:10.51513/jitsa.957371

Derleme

Derin öğrenme tabanlı nesne algılama işlemlerinin farklı uygulama alanları

Yıl 2021, Cilt: 4 Sayı: 2, 148 - 164, 29.11.2021

Sevcan Turan Bahar Milani Feyzullah Temurtaş

https://doi.org/10.51513/jitsa.957371

Cited By: 1

Öz

Otomasyon, insan yaşamını ve çalışma koşullarını kolaylaştırmak için günlük yaşamda ve iş hayatında yaygınlaşmaktadır. Robotlar, sürücüsüz arabalar, insansız araçlar, robot kollar, otomatik fabrikalar vs. hayatımıza hızla girmektedir. Bu otomatikleştirilmiş aktörler için önemli görevlerden biri, çalışılacak ortamdaki nesneleri ve engelleri tanımaktır. Nesne algılama -nesnelerin cinsinin ve ortamdaki konumlarının belirlenmesi- bu görev için en önemli çözümlerden biridir. Evrişimli Sinir Ağı ve GPU işleme gibi derin öğrenme teknikleri ile nesne algılama işlemleri daha doğru ve hızlı sonuç üretmeye başlamış ve araştırmacıların dikkatini çekmiştir. Son yıllarda nesne algılama algoritmaları ve nesne algılamanın kullanımı ile ilgili birçok makale yayınlanmıştır. Nesne algılama algoritmaları hakkında inceleme makaleleri de bulunmaktadır, ancak genel itibariyle algoritmaları tanıtmış ve çok yaygın olarak bilinen uygulama alanlarına odaklanmışlardır. Diğer inceleme makalelerinden farklı olarak, bu çalışmada nesne algılama algoritmalarının çok geniş ve farklı uygulama alanına sahip olduğu gösterilmek istenmektedir. Çalışmada, derin öğrenmeye kısa bir giriş yapıldıktan sonra derin öğrenmeye dayalı standart nesne algılama algoritmaları ve bunların son yıllarda farklı araştırma alanlarındaki uygulamalarına yer verilerek gelecekteki çalışmalar için rehber olmak amaçlanmaktadır. Ayrıca makalelerde kullanılan veri setleri ve değerlendirme ölçütleri de listelenmiştir.

Anahtar Kelimeler

Nesne algılama, Derin öğrenme, CNN, LSTM

Kaynakça

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Chen, J.-W., Lin, W.-J., Cheng, H.-J., Hung, C.-L., Lin, C.-Y., & Chen, S.-P. (2021). A Smartphone-Based Application for Scale Pest Detection Using Multiple-Object Detection Methods. Electronics, 10(4), 372. https://doi.org/10.3390/electronics10040372
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Han, F., Yao, J., Zhu, H., & Wang, C. (2020). Underwater Image Processing and Object Detection Based on Deep CNN Method. Journal of Sensors, 2020, 1–20. https://doi.org/10.1155/2020/6707328
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DIFFERENT APPLICATION AREAS OF OBJECT DETECTION WITH DEEP LEARNING

Yıl 2021, Cilt: 4 Sayı: 2, 148 - 164, 29.11.2021

Sevcan Turan Bahar Milani Feyzullah Temurtaş

https://doi.org/10.51513/jitsa.957371

Cited By: 1

Öz

Automation is spread in all daily life and business activities to facilitate human life and working conditions. Robots, automated cars, unmanned vehicles, robot arms, automated factories etc. are getting place in our lives. For these automated actors, one important task is recognizing objects and obstacles in the target environment. Object detection, determining the objects and their location in the environment, is one of the most important solution for this task. With deep learning techniques like Convolutional Neural Network and GPU processing, object detection has become more accurate and faster, and getting attention of researchers. In recent years, many articles about object detection algorithms and usage of object detection have been published. There are surveys about the object detection algorithms, but they have introduced algorithms and focused on common application areas. With this survey, we aim to show that object detection algorithms have very large and different application area. In this study, we have given a brief introduction to deep learning. We have then focused on standard object detection algorithms based on deep learning and their applications in different research areas in recent years to give an idea for future works. Also, the datasets and evaluation metrics used in the research are listed.

Anahtar Kelimeler

Object detection, deep learning, CNN, LSTM

Kaynakça

Bahdanau, D., Chorowski, J., Serdyuk, D., Brakel, P., & Bengio, Y. (2016). End-to-end attention-based large vocabulary speech recognition. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4945–4949. https://doi.org/10.1109/ICASSP.2016.7472618
Caicedo, J. C., Goodman, A., Karhohs, K. W., Cimini, B. A., Ackerman, J., Haghighi, M., Heng, C. K., Becker, T., Doan, M., McQuin, C., Rohban, M., Singh, S., & Carpenter, A. E. (2019). Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl. Nature Methods. https://doi.org/10.1038/s41592-019-0612-7
Cevikalp, H., Benligiray, B., & Gerek, O. N. (2020). Semi-supervised robust deep neural networks for multi-label image classification. Pattern Recognition, 100, 107164. https://doi.org/https://doi.org/10.1016/j.patcog.2019.107164
Chen, J.-W., Lin, W.-J., Cheng, H.-J., Hung, C.-L., Lin, C.-Y., & Chen, S.-P. (2021). A Smartphone-Based Application for Scale Pest Detection Using Multiple-Object Detection Methods. Electronics, 10(4), 372. https://doi.org/10.3390/electronics10040372
Deng, J., Shi, S., Li, P., Zhou, W., Zhang, Y., & Li, H. (2020). Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection. ArXiv:2012.15712.
Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. https://doi.org/https://doi.org/10.1016/0364-0213(90)90002-E
Face Masks. (2020). https://www.kaggle.com/andrewmvd/face-mask-detection
Fan, D. P., Ji, G. P., Sun, G., Cheng, M. M., Shen, J., & Shao, L. (2020). Camouflaged object detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR42600.2020.00285
Geiger, A., Lenz, P., & Urtasun, R. (2012). Are we ready for autonomous driving? The KITTI vision benchmark suite. 2012 IEEE Conference on Computer Vision and Pattern Recognition, 3354–3361. https://doi.org/10.1109/CVPR.2012.6248074
Gers, F. A., Schmidhuber, J., & Cummins, F. (1999). Learning to forget: continual prediction with LSTM. 1999 Ninth International Conference on Artificial Neural Networks ICANN 99. (Conf. Publ. No. 470), 2, 850–855 vol.2. https://doi.org/10.1049/cp:19991218
Girshick, R, Donahue, J., Darrell, T., & Malik, J. (2016). Region-Based Convolutional Networks for Accurate Object Detection and Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(1), 142–158. https://doi.org/10.1109/TPAMI.2015.2437384
Girshick, Ross. (2015). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV).
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. https://doi.org/10.1007/s11069-020-04315-y
Han, F., Yao, J., Zhu, H., & Wang, C. (2020). Underwater Image Processing and Object Detection Based on Deep CNN Method. Journal of Sensors, 2020, 1–20. https://doi.org/10.1155/2020/6707328
Han, W., Zhang, Z., Caine, B., Yang, B., Sprunk, C., Alsharif, O., Ngiam, J., Vasudevan, V., Shlens, J., & Chen, Z. (2020). Streaming Object Detection for 3-D Point Clouds. http://arxiv.org/abs/2005.01864
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation. https://doi.org/10.1162/neco.1997.9.8.1735
Houben, S., Stallkamp, J., Salmen, J., Schlipsing, M., & Igel, C. (2013). Detection of traffic signs in real-world images: The German traffic sign detection benchmark. The 2013 International Joint Conference on Neural Networks (IJCNN), 1–8. https://doi.org/10.1109/IJCNN.2013.6706807
Hung, J., Goodman, A., Ravel, D., Lopes, S. C. P., Rangel, G. W., Nery, O. A., Malleret, B., Nosten, F., Lacerda, M. V. G., Ferreira, M. U., Rénia, L., Duraisingh, M. T., Costa, F. T. M., Marti, M., & Carpenter, A. E. (2020). Keras R-CNN: library for cell detection in biological images using deep neural networks. BMC Bioinformatics, 21(1), 300. https://doi.org/10.1186/s12859-020-03635-x
Indolia, S., Goswami, A. K., Mishra, S. P., & Asopa, P. (2018). Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach. Procedia Computer Science. https://doi.org/10.1016/j.procs.2018.05.069
Jelodar, H., Wang, Y., Orji, R., & Huang, H. (2020). Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach. ArXiv:2004.11695.
Jing, R., Liu, S., Gong, Z., Wang, Z., Guan, H., Gautam, A., & Zhao, W. (2020). Object-based change detection for VHR remote sensing images based on a Trisiamese-LSTM. International Journal of Remote Sensing, 41(16), 6209–6231. https://doi.org/10.1080/01431161.2020.1734253
Kechaou, A., Martinez, M., Haurilet, M., & Stiefelhagen, R. (2020). Detective: An Attentive Recurrent Model for Sparse Object Detection. http://arxiv.org/abs/2004.12197
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Toplam 68 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	İngilizce
Konular	Mühendislik
Bölüm	Makaleler
Yazarlar	Sevcan Turan 0000-0003-4278-7406 Bahar Milani 0000-0002-5295-4215 Feyzullah Temurtaş 0000-0002-3158-4032
Yayımlanma Tarihi	29 Kasım 2021
Gönderilme Tarihi	27 Haziran 2021
Kabul Tarihi	31 Ağustos 2021
Yayımlandığı Sayı	Yıl 2021 Cilt: 4 Sayı: 2

Kaynak Göster

APA	Turan, S., Milani, B., & Temurtaş, F. (2021). DIFFERENT APPLICATION AREAS OF OBJECT DETECTION WITH DEEP LEARNING. Akıllı Ulaşım Sistemleri Ve Uygulamaları Dergisi, 4(2), 148-164. https://doi.org/10.51513/jitsa.957371

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