Improving the effectiveness of training the on-board object detection system for a compact unmanned aerial vehicle

Москаленко, В`ячеслав Васильович; Dovbysh, A. S.; Naumenko, Igor; Moskalenko, Alona; Коробов, Артем Геннадійович

doi:10.15587/1729-4061.2018.139923

Cited by 3 publications

(8 citation statements)

References 13 publications

(24 reference statements)

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“…In work [10], it was proposed to scan of the normalized high-level feature map by a sliding window, in each position of which classification analysis was carried out. In research work [11], it was proposed to carry out classification analysis of a high-level feature representation using information-extreme decision rules. The main idea of this approach is to transform input space of primary features into binary Hamming space where building radial-basis decision rules.…”

Section: Review Of the Literaturementioning

confidence: 99%

“…In addition, it is proposed to carry out such classification analysis of the feature map in the framework of boosting and the so-called informationextreme technology. This makes it possible to synthesize a classifier with low computational complexity and relatively high accuracy under of limited training sets size constraint [11].…”

Section: Non-maximum Suppressionmentioning

confidence: 99%

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A Model and Training Algorithm of Small-Sized Object Detection System for a Compact Aerial Drone

Москаленко

Moskalenko

Коробов

et al. 2019

Radio Electronics, Computer Science, Control

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Context. Lightweight model and effective training algorithm of on-board object detection system for a compact drone are developed. The object of research is the process of small object detection on aerial images under computational resource constraint and uncertainty caused by small amount of labeled training data. The subject of the research are the model and training algorithm for detecting small objects on aerial imagery. Objective. Goal of the research is developing efficient model and training algorithm of object detection system for a compact aerial drone under conditions of restricted computing resources and the limited volume of the labeled training set. Methods. The four-stage training 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 pre-trained on the ImageNet dataset for reusing takes place. The second stage involves unsupervised training of high-level convolutional sparse coding layers using 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 training datasets for the adaptation of the high-level feature description to the domain application area. At the third stage, there are reduction of output feture map using principan component analysis and building of decision rules. In this case, output feature map is formed by concatenation of feature maps from different level of deep network using upscaling upper maps to uniform shape for each channel. This approach provides more contextual information for efficient recognition of small objects on aerial images. In order to build classifier of output feature map pixels is proposed to use boosted information-extreme learning. Besides that, the extreme learning machine is used to build of regression model for predict bounding box of detected object. Last stage involves fine-tuning of high-level layers of deep network using simulated annealing metaheuristic algorithm in order to approximating the global optimum of complex criterion of training efficiency of detection model. Results. The results obtained on open datasets testify to the suitability of the model and training algorithm for practical usage. The proposed training algorithm utilize 500 unlabeled and 200 labeled training samples to provide 96% correctly detection of objects on the images of the test dataset. Conclusions. Scientific novelty of the paper is a new model and training algorithm for object detection, which enable to achieve high confidence of recognition of small objects on aerial images under computational resource constraint and limited volume of the labeled training set. Practical significance of the paper results consists in the developed model and training algorithm made it possible to reduce requirements for size of labeled training set and computation resources of on-board detection system of aerial drone in tr...

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Section: Review Of the Literaturementioning

confidence: 99%

Section: Non-maximum Suppressionmentioning

confidence: 99%

A Model and Training Algorithm of Small-Sized Object Detection System for a Compact Aerial Drone

Москаленко

Moskalenko

Коробов

et al. 2019

Radio Electronics, Computer Science, Control

View full text Add to dashboard Cite

show abstract

“…However, fast threshold optimization methods for feature encoding have not yet been proposed. In this case, feature inductions based on decision tree and boosting are two particularly promising algorithms which can speed up threshold optimization for binary feature encoding [12,13].…”

Section: Of 14mentioning

confidence: 99%

“…node w s0 and its topological neighbors (the nodes connected with it by the edge) are displaced in the direction to input vector x according to the Oja's rule [13] by:…”

Section: Training Algorithm Designmentioning

confidence: 99%

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The Model and Training Algorithm of Compact Drone Autonomous Visual Navigation System

Москаленко

Moskalenko

Коробов

et al. 2018

Data

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Trainable visual navigation systems based on deep learning demonstrate potential for robustness of onboard camera parameters and challenging environment. However, a deep model requires substantial computational resources and large labelled training sets for successful training. Implementation of the autonomous navigation and training-based fast adaptation to the new environment for a compact drone is a complicated task. The article describes an original model and training algorithms adapted to the limited volume of labelled training set and constrained computational resource. This model consists of a convolutional neural network for visual feature extraction, extreme-learning machine for estimating the position displacement and boosted information-extreme classifier for obstacle prediction. To perform unsupervised training of the convolution filters with a growing sparse-coding neural gas algorithm, supervised learning algorithms to construct the decision rules with simulated annealing search algorithm used for finetuning are proposed. The use of complex criterion for parameter optimization of the feature extractor model is considered. The resulting approach performs better trajectory reconstruction than the well-known ORB-SLAM. In particular, for sequence 7 from the KITTI dataset, the translation error is reduced by nearly 65.6% under the frame rate 10 frame per second. Besides, testing on the independent TUM sequence shot outdoors produces a translation error not exceeding 6% and a rotation error not exceeding 3.68 degrees per 100 m. Testing was carried out on the Raspberry Pi 3+ single-board computer.

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Information-Extreme Machine Training System of Functional Diagnosis System With Hierarchical Data Structure

Shelehov

Barchenko

Prylepa

et al. 2022

RIC

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Context. The problem of information-extreme machine learning of the functional diagnosis system is considered by the example of recognizing the technical state of a laser printer by typical defects of the printed material. The object of the research is the process of hierarchical machine learning of the functional diagnosis system of an electromechanical device. Objective. The main objective is to improve the functional efficiency of machine learning during functional diagnostics system retraining using automatically forming a new hierarchical data structure for an expanded alphabet of recognition classes. Method. A method of information-extreme hierarchical machine learning of the system of functional diagnosis of a laser printer based on typical defects of the printed material is proposed. The method was developed with functional approach of modeling the cognitive processes of natural intelligence, which makes it possible to give the diagnostic system the properties of adaptability under arbitrary initial conditions for the formation of images of printing defects and flexibility during retraining of the system due to an increase in the power of the alphabet of recognition classes. The method is based on the principle of maximizing the amount of information in the process of machine learning. The process of information-extreme machine learning is considered as an iterative procedure for optimizing the parameters of the functioning of the functional diagnostics system according to the information criterion. As a criterion for optimizing machine learning parameters, a modified Kullback’s information measure is considered, which is a functional of the exact characteristics of classification solutions. According to the proposed categorical functional model, an information-extreme machine learning algorithm has been developed based on a hierarchical data structure in the form of a binary decomposition tree. The use of such a data structure makes it possible to split a large number of recognition classes into pairs of nearest neighbors, for which the optimization of machine learning parameters is carried out according to a linear algorithm of the required depth. Results. Information, algorithmic software for the system of functional diagnostics of a laser printer based on images of typical defects in printed material has been developed. The influence of machine learning parameters on the functional efficiency of the system of functional diagnostics of a laser printer based on images of defects in printed material has been investigated. Conclusions. The results of physical modeling have confirmed the efficiency of the proposed method of information-extreme machine learning of the system of functional diagnosis of a laser printer based on typical defects in printed material and can be recommended for practical use. The prospect of increasing the functional efficiency of information-extremal learning of the functional diagnostics system is to increase the depth of machine learning by optimizing additional parameters of the system’s functions, including the parameters of the formation of the input training matrix.

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Improving the effectiveness of training the on-board object detection system for a compact unmanned aerial vehicle

Cited by 3 publications

References 13 publications

A Model and Training Algorithm of Small-Sized Object Detection System for a Compact Aerial Drone

A Model and Training Algorithm of Small-Sized Object Detection System for a Compact Aerial Drone

The Model and Training Algorithm of Compact Drone Autonomous Visual Navigation System

Information-Extreme Machine Training System of Functional Diagnosis System With Hierarchical Data Structure

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