• Journal of Convergence for Information Technology
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• v.11 no.9
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• pp.28-34
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• 2021

In the case of object detection using deep learning, both accuracy and real-time are required. However, it is difficult to use a deep learning model that processes a large amount of data in a limited resource environment. To solve this problem, this paper proposes an object detection model for small embedded devices. Unlike the general detection model, the model size was minimized by using a structure in which the pre-trained feature extractor was removed. The structure of the model was designed by repeatedly stacking lightweight convolution blocks. In addition, the number of region proposals is greatly reduced to reduce detection overhead. The proposed model was trained and evaluated using the public dataset PASCAL VOC. For quantitative evaluation of the model, detection performance was measured with average precision used in the detection field. And the detection speed was measured in a Raspberry Pi similar to an actual embedded device. Through the experiment, we achieved improved accuracy and faster reasoning speed compared to the existing detection method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.81-89
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• 2021

Under natural conditions, Apple detection has the problems of occlusion and small object detection difficulties. This paper proposes an improved model based on SSD. The SSD backbone network VGG16 is replaced with the ResNet50 network model, and the receptive field structure RFB structure is introduced. The RFB model amplifies the feature information of small objects and improves the detection accuracy of small objects. Combined with the attention mechanism (SE) to filter out the information that needs to be retained, the semantic information of the detection objectis enhanced. An improved SSD algorithm is trained on the VOC2007 data set. Compared with SSD, the improved algorithm has increased the accuracy of occlusion and small object detection by 3.4% and 3.9%. The algorithm has improved the false detection rate and missed detection rate. The improved algorithm proposed in this paper has higher efficiency.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.157-161
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• 1996

In the geometric accuracy, most of studies have been concentrated on the analysis of the geometric error, or a control path of grinding using the value of measured geometric error. In this paper, by using the value of measured motor current through hall sensor, detection of the geometric error have been accomplished, and in-process control path of grinding for improvement geometric accuracy, too.

• Journal of Navigation and Port Research
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• v.40 no.5
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• pp.271-278
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• 2016

Localized atmospheric conditions between multi-reference stations can bring the tropospheric delay irregularity that becomes an error terms affecting positioning accuracy in network RTK environment. Imbalanced network error can affect the network solutions and it can corrupt the entire network solution and degrade the correction accuracy. If an anomaly could be detected before the correction message was generated, it is possible to eliminate the anomalous satellite that can cause degradation of the network solution during the tropospheric delay anomaly. An atmospheric grid that consists of four meteorological stations was used to detect an inhomogeneous weather conditions and tropospheric anomaly applied AWSs (automatic weather stations) meteorological data. The threshold of anomaly detection algorithm was determined based on the statistical weather data of AWSs for 5 years in an atmospheric grid. From the analytic results of anomaly detection algorithm it showed that the proposed algorithm can detect an anomalous satellite with an anomaly flag generation caused tropospheric delay anomaly during localized atmospheric conditions between stations. It was shown that the different precipitation condition between stations is the main factor affecting tropospheric anomalies.

• Journal of Sensor Science and Technology
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• v.8 no.1
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• pp.63-68
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• 1999

We proposed a method of eye pattern detection in the 2-D image which was obtained by CCD video camera. To detect face region and eye pattern, we proposed pattern search network and batch SVD algorithm which had the statistical equivalence of PCA. We also used HMM to improve the accuracy of detection. As a result, we acknowledged that the proposed algorithm was superior to PCA pattern detection algorithm in computational cost and accuracy of defection. Furthermore, we evaluated that the proposed algorithm was possible in real-time face pattern detection with 2 frame images per second.

• Journal of Broadcast Engineering
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• v.25 no.4
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• pp.576-586
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• 2020

Analyzing baseball games using sensors such as radars or riders is expensive. In this paper, we propose an algorithm to detect pitch shots and hit shots using baseball video and to generate ball trajectories within hit shots using camera movement. After the pitch shot and the hit shot detection using object detection and optical flow, we generate the transformation relationship between frames and ball locations in the frame, and calculates the ball trajectory. The performance of the proposed method is evaluated for three KBO baseball video sequences, and the detection accuracy and detection rate of pitch shot and hit shot were within 89-95 [%], and the average error for shot range was 13.6[m], The direction error was 7.5° and foul classification accuracy was 98.6%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5095-5111
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• 2019

A multi-rotor Unmanned Aerial Vehicle (UAV) system is developed to solve the manhole cover detection problem for the infrastructure maintenance in the suburbs of big city. The visible light sensor is employed to collect the ground image data and a series of image processing and machine learning methods are used to detect the manhole cover. First, the image enhancement technique is employed to improve the imaging effect of visible light camera. An imaging environment perception method is used to increase the computation robustness: the blind Image Quality Evaluation Metrics (IQEMs) are used to percept the imaging environment and select the images which have a high imaging definition for the following computation. Because of its excellent processing effect the adaptive Multiple Scale Retinex (MSR) is used to enhance the imaging quality. Second, the Single Shot multi-box Detector (SSD) method is utilized to identify the manhole cover for its stable processing effect. Third, the spatial coordinate of manhole cover is also estimated from the ground image. The practical applications have verified the outdoor environment adaptability of proposed algorithm and the target detection correctness of proposed system. The detection accuracy can reach 99% and the positioning accuracy is about 0.7 meters.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.4
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• pp.237-250
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• 2015

This paper describes a hardware-oriented algorithm and its conceptual implementation in a real-time speed limit traffic sign detection system on an automotive-oriented field-programmable gate array (FPGA). It solves the training and color dependence problems found in other research, which saw reduced recognition accuracy under unlearned conditions when color has changed. The algorithm is applicable to various platforms, such as color or grayscale cameras, high-resolution (4K) or low-resolution (VGA) cameras, and high-end or low-end FPGAs. It is also robust under various conditions, such as daytime, night time, and on rainy nights, and is adaptable to various countries' speed limit traffic sign systems. The speed limit traffic sign candidates on each grayscale video frame are detected through two simple computational stages using global luminosity and local pixel direction. Pipeline implementation using results-sharing on overlap, application of a RAM-based shift register, and optimization of scan window sizes results in a small but high-performance implementation. The proposed system matches the processing speed requirement for a 60 fps system. The speed limit traffic sign recognition system achieves better than 98% accuracy in detection and recognition, even under difficult conditions such as rainy nights, and is implementable on the low-end, low-cost Xilinx Zynq automotive Z7020 FPGA.

• The Journal of the Acoustical Society of Korea
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• v.30 no.2
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• pp.73-79
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• 2011

This paper presents a new impulsive noise detection algorithm in speech signal. The proposed method employs the frequency domain characteristic of the impulsive noise to improve the detection accuracy while avoiding the false-alarm problem by the pitch of the speech signal. Furthermore, we proposed time-frequency domain impulsive noise detector that utilizes both the time and frequency domain parameters which minimizes the false-alarm problem by mutually complementing each other. As the result, the proposed time-frequency domain detector shows the best performance with 99.33 % of detection accuracy and 1.49 % of false-alarm rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.7
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• pp.1429-1436
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• 2009

In this paper, we propose a robust eye detection method using template matching based on genetic algorithm in the single facial image. The previous works for detecting pupil using genetic algorithm had a problem that the detection accuracy is influnced much by the initial population for it's random value. Therefore, their detection result is not consistent. In order to overcome this point we extract local minima in the facial image and generate initial populations using ones that have high fitness with a template. Each chromosome consists of geometrical informations for the template image. Eye position is detected by template matching. Experiment results verify that the proposed eye detection method improve the precision rate and high accuracy in the single facial image.