В’ячеслав Васильович Москаленко, Альона Сергіївна Москаленко, Артем Геннадійович Коробов, Микола Олександрович Зарецький, Віктор Анатолійович Семашко


The efficient model and learning algorithm of the small object detection system for compact aerial vehicle under conditions of restricted computing resources and the limited volume of the labeled learning set are developed. The four-stage learning algorithm of the object detector is proposed. At the first stage, selecting the type of deep convolutional neural network and the number of low-level layers that is pretrained on the ImageNet dataset for reusing takes place. The second stage involves unsupervised learning of high-level convolutional sparse coding layers using the modification of growing neural gas to automatically determine the required number of neurons and provide optimal distributions of the neurons over the data. Its application makes it possible to utilize the unlabeled learning datasets for the adaptation of the high-level feature description to the domain application area. At the third stage, the output feature map is formed by concatenation of feature maps from the different level of the deep convolutional neural network. At that, there is a reduction of output feature map using principal component analysis and followed by the building of decision rules. In order to perform the classification analysis of output, feature map is proposed to use information-extreme classifier learning on principles of boosting. Besides that, the orthogonal incremental extreme learning machine is used to build the regression model for the predict bounding box of the detected small object. The last stage involves fine-tuning of high-level layers of deep network using simulated annealing metaheuristic algorithm in order to approximate the global optimum of the complex criterion of learning efficiency of detection model. As a result of the use of proposed approach has been achieved 96% correctly detection of objects on the images of the open test dataset which indicates the suitability of the model and learning algorithm for practical use. In this case, the size of the learning dataset that has been used to construct the model was 500 unlabeled and 200 labeled learning samples


growing neural gas; convolutional neural network; objects detector; information criterion; simulated annealing algorithm; extreme learning


Subbotin, S. O. The special deep neural network for stationary signal spectra classification. 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET), Lviv-Slavske, Ukraine, 20-24 Feb, 2018, pp. 123–128. DOI:10.1155/2017/3296874.

Carrio, A., Sampedro, C., Rodriguez-Ramos, A., Campoy, P. A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles. Journal of Sensors, 2017, vol. 2017, pp. 1–13. DOI: 10.1155/2017/3296874.

Xu, X., Ding, Y., Hu, S. X. Scaling for edge inference of deep neural networks. Nature Electronics, 2018, vol. 1, no. 4, pp. 216-222. DOI:10.1038/s41928-018-0059-3.

Loquercio, A., Maqueda, A. I., Del-Blanco, C. R., Scaramuzza, D. DroNet: Learning to Fly by Driving. IEEE Robotics and Automation Letters, 2018, vol. 3, no. 2, pp. 1088–1095. DOI: 10.1145/2733373.2806332.

Mathew, A., Mathew, J., Govind, M., Mooppan, A. An Improved Transfer learning Approach for Intrusion Detection. Procedia Computer Science, 2017, vol. 115, pp. 251–257. doi:10.1016/j.procs.2017.09.132.

Radenović, F., Tolias, G., Chum, O. Fine-tuning CNN Image Retrieval with No Human Annotation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018. Available at: https://ieeexplore.ieee.org/document/8382272. (Accessed 03.12.2018). DOI: 10.1109/TPAMI.2018.2846566.

Moskalenko, V. V., Dovbysh, A. S., Naumenko, I. V., Moskalenko, A. S., Korobov, A. G. Improving the effectiveness of training the on-board object detection system for a compact unmanned aerial vehicle. Eastern-European Journal of Enterprise Technologies, 2018, vol. 4/9 no. 94, pp. 19–26. DOI: 10.15587/1729-4061.2018.139923.

Vens, C., Costa, F., Random Forest Based Feature Induction. IEEE 11th International Conference on Data Mining, Vancouver, Canada, 11-14 Dec, 2011, pp. 744–753. DOI: 10.1109/ICDM.2011.121.

Feng, Q., Chen, C. L. P., Chen, L. Compressed auto-encoder building block for deep learning network. 3rd International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS), Jinzhou, Liaoning, China, 26-29 Aug, 2016, pp. 131–136. DOI: 10.1109/ICCSS.2016.7586437.

Labusch, K., Barth, E., Martinetz, T. Sparse coding neural gas: learning of overcomplete data representations. Neurocomputing, 2009, vol. 72, no. 7–9, pp. 1547–1555. DOI:10.1016/j.neucom.2008.11.027.

Mrazova, I., Kukacka, M. Image Classification with Growing Neural Networks. International Journal of Computer Theory and Engineering, 2013, vol. 5, no. 3, pp. 422–427. DOI:10.7763/IJCTE.2013.V5.722.

Palomo, J., López-Rubio, E. The Growing Hierarchical Neural Gas Self-Organizing Neural Network. IEEE Transactions on Neural Networks and Learning Systems, 2017, vol. 28, no. 9, pp. 2000–2009. DOI: 10.1109/TNNLS.2016.2570124.

Nakahara, H., Yonekawa, H., Sato, S. An object detector based on multiscale sliding window search using a fully pipelined binarized CNN on an FPGA. International Conference on Field Programmable Technology (ICFPT), Melbourne, 11-13 December, 2018, pp. 168–175. DOI: 10.1109/FPT.2017.8280135.

Chen, X., Xiang, S., Liu, C.-L., Pan, C.-H. Aircraft Detection by Deep Convolutional Neural Networks. IPSJ Transactions on Computer Vision

and Applications, 2015, vol. 7, pp. 10–17. DOI: 10.2197/ipsjtcva.7.10.

Zou, W., Xia, Y., Li, H. Fault Diagnosis of Tennessee-Eastman Process Using Orthogonal Incremental Extreme Learning Machine Based on Driving Amount. IEEE Transactions on Cybernetics, 2018, vol. 48, no. 12, pp. 1-8. DOI: 10.1109/TCYB.2018.2830338.

Rere, R. L. M., Fanany, M. I., Arymurthy, A. M. Metaheuristic Algorithms for Convolution Neural Network. Computational Intelligence and Neuroscience, 2017, vol. 2016, pp. 1–13. DOI:10.1155/2016/1537325.

Patricia, N., Caputo, B. Learning to Learn, from Transfer Learning to Domain Adaptation: A Unifying Perspective. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, 23-28 June, 2014, pp. 1442–1449. DOI: 10.1109/CVPR.2014.187.

Nguyen, A., Yosinski, J., Clune, J. Deep neural networks are easily fooled: High confidence pre-dictions for unrecognizable images. IEEE Conference on Computer Vision and Pattern Recognition, (CVPR), Boston, MA, 7-12 June, 2015, pp. 427–436. DOI: 10.1109/CVPR.2015.7298640.

Ayumi, V., Rere, L. M. R., Fanany, M. I., Ar-ymurthy, A. M. Optimization of convolutional neural network using microcanonical annealing algorithm. International Conference on Advanced Computer Science and Information Systems (ICACSIS), Malang, Indonesia, 15-16 Oct., 2016, pp. 506–511. DOI: 10.1109/ICACSIS.2016.7872787.

Maggiori, E., Tarabalka, Y., Charpiat, G., Alliez, P. High-Resolution Aerial Image Labeling With Convolutional Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 2017, vol. 55, no. 12, pp. 7092–7103. DOI:10.1109/TGRS.2017.2740362.

DOI: https://doi.org/10.32620/reks.2018.4.04


  • There are currently no refbacks.