Robotik Uygulamalar İçin Derin Öğrenme Tabanlı Nesne Tespiti ve Sınıflandırması

Year 2020, Volume: 10 Issue: 1, 205 - 213, 15.06.2020

Ferdi Özbilgin , Cengiz Tepe

https://doi.org/10.31466/kfbd.734393

Cited By: 2

Abstract

Görüntü içerisindeki nesnelerin tespit edilmesi ve sınıflandırılma uygulamaları her geçen gün artmaktadır. Bu çalışmada da robotik uygulamalarda da kullanılabilecek bir nesne tespiti ve sınıflandırılması uygulaması gerçekleştirilmiştir. Alexnet Evrişimsel Sinir Ağları (ESA) mimarisi ve Bölgesel Evrişimsel Sinir Ağları (B-ESA) algoritması ile gerçekleştirilen çalışmada yedi farklı nesne sınıfı seçilmiştir. Veri setindeki 684 eğitim verisi etiketlenerek ağın eğitilmesinde kullanılmıştır. 226 test görüntüsü eğitilen ağda test edilmesi sonucunda her sınıfa ait doğru tahmin değerleri ve toplam doğruluk değerleri bulunmuştur. Sınıflara ait tahminlerde en düşük %85,74 ve en yüksek %100 değerlerine ulaşılmıştır. Tüm test verileri için doğruluk değeri %93,81 bulunmuştur.

Keywords

Nesne tespiti, Sınıflandırma, Derin öğrenme

Deep Learning Based Object Detection and Classification for Robotic Applications

Abstract

The detection and classification applications of the objects in the image are increasing day by day. In this study, an object detection and classification application, which can also be used in robotic applications, has been realized. Seven different object classes were selected in the study conducted with Alexnet Evolutionary Neural Networks (CNN) architecture and Regional Convolutional Neural Networks (R-CNN) algorithm. 684 training data in the data set were labelled and used to train the network. As a result of testing 226 test images in the trained network, correct predictive values and total accuracy values of each class were found. The lowest estimates of 85.74% and the highest 100% were reached in the estimates of the classes. The accuracy value was 93.81% for all test data.

Keywords

Object detection, Classification, Deep learning

References

• Çoban, M., Cubukcu, B., Yüzgeç, U. 2019. Nesne Takibi Yapan Robot Uygulaması (RasPiBot) Object Tracking Robot Application (RasPiBot).
• Do, T.-T., Nguyen, A., Reid, I. 2018. Affordancenet: An end-to-end deep learning approach for object affordance detection. 2018 IEEE international conference on robotics and automation (ICRA). IEEE. pp. 1-5.
• Espinosa, J.E., Velastin, S.A., Branch, J.W. 2017. Vehicle detection using alex net and faster R-CNN deep learning models: a comparative study. International Visual Informatics Conference. Springer. pp. 3-15.
• Fu, C.-Y., Liu, W., Ranga, A., Tyagi, A., Berg, A.C. 2017. Dssd: Deconvolutional single shot detector. arXiv preprint arXiv:1701.06659.
• Griffin, G., Holub, A., Perona, P. 2007. Caltech-256 object category dataset.
• Gu, S., Ding, L., Yang, Y., Chen, X. 2017. A new deep learning method based on AlexNet model and SSD model for tennis ball recognition. 2017 IEEE 10th International Workshop on Computational Intelligence and Applications (IWCIA). IEEE. pp. 159-164.
• Han, X., Zhong, Y., Cao, L., Zhang, L. 2017. Pre-trained alexnet architecture with pyramid pooling and supervision for high spatial resolution remote sensing image scene classification. Remote Sensing, 9(8), 848.
• Haque, M.F., Lim, H.-Y., Kang, D.-S. 2019. Object Detection Based on VGG with ResNet Network. 2019 International Conference on Electronics, Information, and Communication (ICEIC). IEEE. pp. 1-3.
• Hariharan, B., Arbeláez, P., Girshick, R., Malik, J. 2014. Simultaneous detection and segmentation. European Conference on Computer Vision. Springer. pp. 297-312.
• Hou, L., Wu, Q., Sun, Q., Yang, H., Li, P. 2016. Fruit recognition based on convolution neural network. 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE. pp. 18-22.
• Huang, L., Ren, K., Fan, C., Deng, H. 2019. A Lite Asymmetric DenseNet for effective object detection based on convolutional neural networks (CNN). Optoelectronic Imaging and Multimedia Technology VI. International Society for Optics and Photonics. pp. 111871T.
• Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T. 2014. Caffe: Convolutional architecture for fast feature embedding. Proceedings of the 22nd ACM international conference on Multimedia. pp. 675-678.
• Kang, K., Li, H., Yan, J., Zeng, X., Yang, B., Xiao, T., Zhang, C., Wang, Z., Wang, R., Wang, X. 2017. T-cnn: Tubelets with convolutional neural networks for object detection from videos. IEEE Transactions on Circuits and Systems for Video Technology, 28(10), 2896-2907.
• Karpathy, A., Fei-Fei, L. 2015. Deep visual-semantic alignments for generating image descriptions. Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3128-3137.
• Khoreva, A., Benenson, R., Ilg, E., Brox, T., Schiele, B. 2019. Lucid data dreaming for video object segmentation. International Journal of Computer Vision, 127(9), 1175-1197.
• Krizhevsky, A., Sutskever, I., Hinton, G.E. 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems. pp. 1097-1105.
• Kutlu, Ö. 2019. İnsansız hava aracı ile elde edilen görüntülerin derin öğrenme yöntemleri ile analizi.
• Lee, S.-H., Yeh, C.-H., Hou, T.-W., Yang, C.-S. 2019. A Lightweight Neural Network Based on AlexNet-SSD Model for Garbage Detection. Proceedings of the 2019 3rd High Performance Computing and Cluster Technologies Conference. pp. 274-278.
• Li, B. 2017. 3d fully convolutional network for vehicle detection in point cloud. 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE. pp. 1513-1518.
• Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., Pietikäinen, M. 2020. Deep learning for generic object detection: A survey. International journal of computer vision, 128(2), 261-318.
• Lu, K., An, X., Li, J., He, H. 2017. Efficient deep network for vision-based object detection in robotic applications. Neurocomputing, 245, 31-45.
• Lv, L., Tan, Y. 2019. Detection of cabinet in equipment floor based on AlexNet and SSD model. The Journal of Engineering, 2019(15), 605-608.
• Tang, P., Wang, C., Wang, X., Liu, W., Zeng, W., Wang, J. 2019. Object detection in videos by high quality object linking. IEEE transactions on pattern analysis and machine intelligence.
• Tomè, D., Monti, F., Baroffio, L., Bondi, L., Tagliasacchi, M., Tubaro, S. 2016. Deep convolutional neural networks for pedestrian detection. Signal processing: image communication, 47, 482-489.
• Voigtlaender, P., Chai, Y., Schroff, F., Adam, H., Leibe, B., Chen, L.-C. 2019. Feelvos: Fast end-to-end embedding learning for video object segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 9481-9490.
• Wang, B., Tang, S., Xiao, J.-B., Yan, Q.-F., Zhang, Y.-D. 2019. Detection and tracking based tubelet generation for video object detection. Journal of Visual Communication and Image Representation, 58, 102-111.
• Zou, Z., Shi, Z., Guo, Y., Ye, J. 2019. Object detection in 20 years: A survey. arXiv preprint arXiv:1905.05055.