Object detection in images with a structural descriptor based on graphs

Zakharov, A. A.; Barinov, A. E.; Zhiznyakov, A. L.; Titov, V. S.

doi:10.18287/2412-6179-2018-42-2-283-290

Cited by 14 publications

(3 citation statements)

References 26 publications

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“…STD technology is widely used in real-life production, such as intelligent transportation [1][2][3], medical image diagnosis in medicine [4][5][6], image retrieval [7][8][9], remote sensing image analysis [10][11][12], and military applications [13][14][15]. Target detection is mainly used to identify the target object from the data to be detected, including the position, shape, size, and color of the object.…”

Section: Introductionmentioning

confidence: 99%

A Small Target Detection Method Based on the Improved FCN Model

2022

Advances in Multimedia

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Traditional object detection is mainly aimed at large objects in images. The main function achieved is to identify the shape, color, and trajectory of the target. However, in practice, small target objects in the image must be detected in addition to large targets. Common small target detection (STD) is mainly used in intelligent transportation, video surveillance, and other fields. Small targets have small size, few pixels, and low resolution and are easily blocked. Small object detection has emerged as a research problem and a hotspot in the field of object detection. This study proposes an improved FCN model based on the full convolutional neural network (FCN) and applies it to the STD. The following is the central concept of the proposed method. Small targets are prone to occlusion and deformation in the image data. The deformation here is mainly reflected in the larger shape obtained by shooting at different angles. Therefore, it is a challenge to fully and accurately obtain the characteristics of small targets. The traditional method based on multilayer feature fusion cannot achieve ideal results for STD. This study is based on FCN and introduces a spatial transformation network. The network can optimize the handling of problems such as partial occlusion or deformation of small objects. The use of a spatial transformation network can alleviate the problem of poor feature extraction caused by partial occlusion or deformation of small targets, improving final detection accuracy. The experimental results on public datasets show that the proposed method outperforms other deep learning algorithms (DLA) in the detection accuracy of small target objects. Furthermore, the model’s training time is reduced. This study’s research provides a good starting point for the detection, recognition, and tracking of some small objects.

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Section: Introductionmentioning

confidence: 99%

A Small Target Detection Method Based on the Improved FCN Model

2022

Advances in Multimedia

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“…In contrast, contour analysis uses the single similarity measure of two vector-contours, the module of the normalized dot product (NDP) that is invariant (insensitive) to transfer, rotation, and proportional scaling of the recognized object towards the reference one. The similar recognition methods produce a unique value per each classes pair [6][7][8] and require an additional clustering procedure to match an object to a certain class. In contrast, the NDP module provides a uniform similarity measure of two contours within the range [0..1] where 1denotes the identical instances.…”

Section: Introductionmentioning

confidence: 99%

Optimal tuning of the contour analysis method to recognize aircraft on remote sensing imagery

Dremov¹,

Miroshnichenko²,

Titov³

2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

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In this paper, we describe the experimental results of aircraft recognition on optical remote sensing imagery using the theory of contour analysis. We propose the a method to calculate optimal values of the contour’s items quantity and the classification threshold through measuring within- and between-class distances for all possible training set instances combinations with followed by detection and minimization of the type I and II errors. We discuss the construction of contours’ similarity measures combining the principles of finding the most appropriate reference instance and calculating the average value for the whole class. It is shown that the proposed parameters' tuning method and the similarity function make contour analysis capable to train on compact non-uniform datasets and to recognize aircraft on the noisy and less detailed images.

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“…Global symptoms determination approach based on fuzzy logic designed to fix this problem. Such an approach in conjunction with image processing algorithms that allows to search objects on images [8,9] or correct images [10] obtained for example from unmanned aerial vehicles could be the basis of a system for automatically determining the forest plantations health state.…”

Section: Introductionmentioning

confidence: 99%

Tree state category identification for boreal area conifers using global features estimation by fuzzy logic approach

Pyataev¹,

Redkin²,

Pyataeva

2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

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Tree state category identification allows forecasting forest development in the surveyed area. Tree state category determination process based on global features is subjective and uses concepts such as the degree of density of the crown, the degree of drying of branches, the fall of the bark, the color of the needles, etc. For global features estimation, fuzzy logic is used. To formalize these subjective concepts, linguistic variables and their terms were extracted. The characteristic functions describing the terms were piecewise linear and in this work were approximated by Gaussian functions. Such an approach in conjunction with image processing algorithms that allows to search objects on images or correct images obtained for example from unmanned aerial vehicles could be the basis of a system for automatically determining the forest plantations health state and improve the inspection quality. The study was conducted for coniferous species of the boreal zone. The mathematical model built in this work allows reducing the cost of automation of calculations related to the processing of the data obtained by forest pathological surveys, despite the fact that the accuracy value of fuzzy classification after the approximation of the membership functions remained at the same level.

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Object detection in images with a structural descriptor based on graphs

Cited by 14 publications

References 26 publications

A Small Target Detection Method Based on the Improved FCN Model

A Small Target Detection Method Based on the Improved FCN Model

Optimal tuning of the contour analysis method to recognize aircraft on remote sensing imagery

Tree state category identification for boreal area conifers using global features estimation by fuzzy logic approach

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