• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.955-961
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• 2010

In this paper, we describe a study of a vehicle detection method based on a Boosting Classifier which uses Histogram of Oriented Gradient (HOG) features and Image Segmentation techniques. An input image is segmented by means of a split and merge algorithm. Then, the two largest segmented regions are removed in order to reduce the search region and speed up processing time. The HOG features are then calculated for each pixel in the search region. In order to detect the vehicle region we used the AdaBoost (adaptive boost) method, which is well known for classifying samples with two classes. To evaluate the performance of the proposed method, 537 training images were used to train and learn the classifier, followed by 500 non-training images to provide the recognition rate. From these experiments we were able to detect the proper image 98.34% of the time for the 500 non-training images. In conclusion, the proposed method can be used for detecting the location of a vehicle in an intelligent vehicle control system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.791-799
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• 2008

An image recognition for surrounding conditions is very important in image transmission. In recently rears, as the information infrastructure is more general, the user-centered demands in which they want to identify by object's states image using wire or wireless environment have increased. However, existing mobile solution could be hard to expect high quality mage, because limitation of software processing according as network based on mobile terminal which has low band width supports software codec. To solve this weak point, this paper describes on hardware codec design based on MPEG-4 which is international video compression standard. Implemented system contains the embedded CPU for optimized design and it works high quality service as transmission speed and resolution in mobile circumstance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.530-533
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• 2009

This paper proposes a method of recognizing that a person doesn't put on a safety cap using image processing method in DSP(Digital Signal Processor). It processes inputted images by image input devices that equipped in a industrial settings. If the method recognizes a person that doesn't put on a safety cap, a system transfers relevant recognition result to a supervisor and takes proper measures. If an accident happens and someone doesn't put on a safety cap, additional casualities could be. Proposed method can nip additional casualties in the bud. To recognize that a person don't put on a safety cap, images are processed by object abstraction, removal of noise, decision of a thing or a person, abstraction of a head part in a image, recognizing whether a man puts on a safety cap using HSV color space or not, and so on. Image input and image process are processed by DSP. And C language-based codes are optimized by an eignefunction(Intrinsics) for speed improvement of algorithms.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.2
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• pp.33-38
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• 2008

Biometric based authentication can provide strong security guarantee about the identity of users. However, security of biometric data is particularly important as compromise of the data will be permanent. Cancelable biometrics stores a non - invertible transformed version of the biometric data. Thus, even if the storage is compromised, the biometric data remains safe. Cancelable biometrics also provide a higher level of privacy by allowing many templates for the same biometric data and hence non-linkability of user's data stored in different databases. In this paper, we proposed the fast polynomial reconstruction algorithm for fuzzy fingerprint vault. The proposed method needs (k+1) real points to reconstruct the polynomial of degree (k-1). It enhances the speed, however, by $300{\sim}1500$ times according to the degree of polynomial compared with the exhaust search.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.427-432
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• 2021

In this paper, we conduct performance analysis in automatic modulation classification of unknown communication signal to identify its modulation types based on deep neural network. The modulation classification performance was verified using time domain digital sample data of the modulated signal, frequency domain data to which FFT was applied, and time and frequency domain mixed data as neural network input data. For 11 types of analog and digitally modulated signals, the modulation classification performance was verified in various SNR environments ranging from -20 to 18 dB and reason for false classification was analyzed. In addition, by checking the learning speed according to the type of input data for neural network, proposed method is effective for constructing an practical automatic modulation recognition system that require a lot of time to learn.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.44-49
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• 2021

Recently, due to COVID-19, studies have been popularly worked to apply neural network to mask wearing automatic detection system. For applying neural networks, the 1-stage detection or 2-stage detection methods are used, and if data are not sufficiently collected, the pretrained neural network models are studied by applying fine-tuning techniques. In this paper, the system is consisted of 2-stage detection method that contain MTCNN model for face recognition and ResNet model for mask detection. The mask detector was experimented by applying five ResNet models to improve accuracy and fps in various environments. Training data used 17,217 images that collected using web crawler, and for inference, we used 1,913 images and two one-minute videos respectively. The experiment showed a high accuracy of 96.39% for images and 92.98% for video, and the speed of inference for video was 10.78fps.

• Journal of IKEEE
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• v.24 no.4
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• pp.1050-1057
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• 2020

Immersive 360-degree media has a problem of slowing down the video recognition speed when the video is processed by the conventional method using a variety of rendering methods, and the video size becomes larger with higher quality and extra-large volume than the existing video. In addition, in most cases, only one scene is captured by fixing the camera in a specific place due to the characteristics of the immersive 360-degree media, it is not necessary to extract feature information from all scenes. In this paper, we propose a reference frame selection method for immersive 360-degree media and describe its application process to copyright protection technology. In the proposed method, three pre-processing processes such as frame extraction of immersive 360 media, frame downsizing, and spherical form rendering are performed. In the rendering process, the video is divided into 16 frames and captured. In the central part where there is much object information, an object is extracted using an RGB vector per pixel and deep learning, and a reference frame is selected using object feature information.

• Journal of Drive and Control
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• v.17 no.4
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• pp.23-30
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• 2020

Driving on irregular and inclined roads using agricultural machinery such as spraying machines or trucks in orchards causes farmer casualties associated with the overturning of agricultural machinery. In addition, the harm to agricultural workers caused by the excessive inhalation of the scattered pesticide frequently occurs during pest control processes. To address these problems, we introduced precision agricultural technology that could selectively spray pesticides only where the fruit is present by recognizing the presence or shape of the fruit in the orchard. In this paper, a 16-channel LIDAR (VLP-16) made of Velodyne was used to identify the shape of fruit trees. Solenoid valves were attached to the end parts of 12 nozzles of the orchard spraying machine for on/off control. The smart spraying machine implemented in this way was mounted on a vehicle capable of autonomous travel and performed selective control depending upon the shape of the fruit trees while traveling in the orchards. This is expected to significantly reduce the amounts of pesticides used in orchards and production costs.

• Journal of Platform Technology
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• v.9 no.4
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• pp.3-9
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• 2021

In this paper, a graph data model is introduced to effectively recognize the situation between each object on the road detected by vehicles or road infrastructure sensors. The proposed method builds a graph database by modeling each object on the road as a node of the graph and the relationship between objects as an edge of the graph, and updates object properties and edge properties in real time. In this case, the relationship between objects represented as edges is set when there is a possibility of approach between objects in consideration of the position, direction, and speed of each object. Finally, we propose a spatial indexing technique for graph nodes and edges to update the road object graph database represented through the proposed graph modeling method continuously in real time. To show the superiority of the proposed indexing technique, we compare the proposed indexing based database update method to the non-indexing update method through simulation. The results of the simulation show the proposed method outperforms more than 10 times to the non-indexing method.

• Smart Structures and Systems
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• v.31 no.5
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• pp.469-483
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• 2023

The dynamic characteristics of wind turbine blades are usually monitored by contact sensors with the disadvantages of high cost, difficult installation, easy damage to the structure, and difficult signal transmission. In view of the above problems, based on computer vision technology and the improved YOLOv5 (You Only Look Once v5) deep learning model, a non-contact dynamic characteristic monitoring method for wind turbine blade is proposed. First, the original YOLOv5l model of the CSP (Cross Stage Partial) structure is improved by introducing the CSP2_2 structure, which reduce the number of residual components to better the network training speed. On this basis, combined with the Deep sort algorithm, the accuracy of structural displacement monitoring is mended. Secondly, for the disadvantage that the deep learning sample dataset is difficult to collect, the blender software is used to model the wind turbine structure with conditions, illuminations and other practical engineering similar environments changed. In addition, incorporated with the image expansion technology, a modeling-based dataset augmentation method is proposed. Finally, the feasibility of the proposed algorithm is verified by experiments followed by the analytical procedure about the influence of YOLOv5 models, lighting conditions and angles on the recognition results. The results show that the improved YOLOv5 deep learning model not only perform well compared with many other YOLOv5 models, but also has high accuracy in vibration monitoring in different environments. The method can accurately identify the dynamic characteristics of wind turbine blades, and therefore can provide a reference for evaluating the condition of wind turbine blades.