• Journal of the Ergonomics Society of Korea
• /
• v.31 no.3
• /
• pp.421-426
• /
• 2012

As headlamp technology advances, newly developed various headlamps were introduced in the market. The objective of this study is to quantitatively analyze the detection distance of the recently developed LED headlamps and existing headlamps, complying with specific technical standard. Background: The detection distance of headlamps is very important to prevent automobile accident at night time. The studies of detection distance of LED, Halogen and HID headlamp have been conducted, but no study has shown the detection distance of pedestrian target with various colors (Black, White, Blue). Method: The experiment of detection distance was conducted with 30 people, which divide into 2 groups as 15 men and 15 women. Automatic transferable target on the rail was manufactured in order to reduce the error of study's result, and ANOVA also conducted to analyze the main effect with sign color, sex and headlamp classified by detection distance. In addition, the luminance by average detection distance was measured as well. Results: The detection distance of headlamps was HID > LED > Halogen. The luminance measure of LED headlamp was lower than HID and Halogen headlamps. Conclusion: The headlamp performs a very significant role for safety at night time but it needs to be improved through assessment of visual characteristics. Also, it needs to be suggested the need of test method for dynamic detection distance concerning technical development is suggested.

• Journal of Information Processing Systems
• /
• v.2 no.1
• /
• pp.44-47
• /
• 2006

One of the critical issues in a sensor network concerns the detection of changes in data streams. Recently presented change detection schemes primarily use a sliding window model to detect changes. In such a model, a distance function is used to compare two sliding windows. Therefore, the performance of the change detection scheme is greatly influenced by the distance function. With regard to sensor nodes, however, energy consumption constitutes a critical design concern because the change detection scheme is implemented in a sensor node, which is a small battery-powered device. In this paper, we present a comparative study of various distance functions in terms of execution time, energy consumption, and detecting accuracy through simulation of speech signal data. The simulation result demonstrates that the Euclidean distance function has the highest performance while consuming a low amount of power. We believe our work is the first attempt to undertake a comparative study of distance functions in terms of execution time, energy consumption, and accuracy detection.

• Journal of Information Processing Systems
• /
• v.10 no.4
• /
• pp.589-601
• /
• 2014

The performance of edge detection often relies on its ability to correctly determine the dissimilarities of connected pixels. For grayscale images, the dissimilarity of two pixels is estimated by a scalar difference of their intensities and for color images, this is done by using the vector difference (color distance) of the three-color components. The Euclidean distance in the RGB color space typically measures a color distance. However, the RGB space is not suitable for edge detection since its color components do not coincide with the information human perception uses to separate objects from backgrounds. In this paper, we propose a novel method for color edge detection by taking advantage of the HSV color space and the Mahalanobis distance. The HSV space models colors in a manner similar to human perception. The Mahalanobis distance independently considers the hue, saturation, and lightness and gives them different degrees of contribution for the measurement of color distances. Therefore, our method is robust against the change of lightness as compared to previous approaches. Furthermore, we will introduce a noise-resistant technique for determining image gradients. Various experiments on simulated and real-world images show that our approach outperforms several existing methods, especially when the images vary in lightness or are corrupted by noise.

• Journal of Korea Multimedia Society
• /
• v.24 no.9
• /
• pp.1242-1250
• /
• 2021

Detection of outliers deviating normal data distribution in high dimensional data is an important technique in many application areas. In this paper, a distance-based outlier detection method using landmarks in high dimensional data is proposed. Given normal training data, the k-means clustering method is applied for the training data in order to extract the centers of the clusters as landmarks which represent normal data distribution. For a test data sample, the distance to the nearest landmark gives the outlier score. In the experiments using high dimensional data such as images and documents, it was shown that the proposed method based on the landmarks of one-tenth of training data can give the comparable outlier detection performance while reducing the time complexity greatly in the testing stage.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.2
• /
• pp.610-630
• /
• 2020

A large number of people suffered from traffic accidents each year, so people pay more attention to traffic safety. However, the traditional methods use laser sensors to calculate the vehicle distance at a very high cost. In this paper, we propose a method based on deep learning to calculate the vehicle distance with a monocular camera. Our method is inexpensive and quite convenient to deploy on the mobile platforms. This paper makes two contributions. First, based on Light-Head RCNN, we propose a new vehicle detection framework called Light-Car Detection which can be used on the mobile platforms. Second, the planar homography of projective geometry is used to calculate the distance between the camera and the vehicles ahead. The results show that our detection system achieves 13FPS detection speed and 60.0% mAP on the Adreno 530 GPU of Samsung Galaxy S7, while only requires 7.1MB of storage space. Compared with the methods existed, the proposed method achieves a better performance.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2017.11a
• /
• pp.944-945
• /
• 2017

Color image edge detection is an important operation in many image processing areas. This paper presents a new method for edge detection based on the Bhattacharyya distance that can handle arbitrary boundaries by exploring several edge patterns. Experiments show promising results compared to some existing methods.

• Proceedings of the IEEK Conference
• /
• 2005.11a
• /
• pp.363-366
• /
• 2005

In this paper, for detecting human faces under variations of lighting condition and facial expression, distance ranking feature and detection algorithm based on the feature are proposed. Distance ranking is the intensity ranking of a distance transformed image. Based on statistically consistent edge information, distance ranking is robust to lighting condition change. The proposed detection algorithm is a matching algorithm based on FFT and a solution of discretization problem in the sliding window methods. In experiments, face detection results in the situation of varying lighting condition, complex background, facial expression change and partial occlusion of face are shown

• IEMEK Journal of Embedded Systems and Applications
• /
• v.17 no.5
• /
• pp.289-296
• /
• 2022

In this paper, we developed a deep learning-based recyclable object detection model. The model is developed based on YOLOv5 that is a one-stage detector. The deep learning model detects and classifies the recyclable object into 7 categories: paper, carton, can, glass, pet, plastic, and vinyl. We propose two methods for recyclable object detection models to solve problems during training. Bounding Box CutMix solved the no-objects training images problem of Mosaic, a data augmentation used in YOLOv5. Standardized Distance-based IoU replaced DIoU using a normalization factor that is not affected by the center point distance of the bounding boxes. The recyclable object detection model showed a final mAP performance of 0.91978 with Bounding Box CutMix and 0.91149 with Standardized Distance-based IoU.

• Bulletin of the Korean Chemical Society
• /
• v.35 no.12
• /
• pp.3547-3552
• /
• 2014

Laser Raman spectroscopy is one of the most powerful technologies for standoff detection of hazardous materials including explosives. Supported by recent development of laser and sensitive ICCD camera, the technology can identify trace amount of unknown substances in a distance. Using this concept, we built a standoff detection system, in which nanosecond pulse laser and nanosecond gating ICCD technique were delicately devised to avoid the large background noise which suppressed weak Raman signals from the target sample. In standoff detection of explosives which have large kill radius, one of the most important technical issues is the detection distance from the target. Hence, we focused to increase the detection distance up to 54 m by careful optimization of optics and laser settings. The Raman spectra of hazardous materials observed at the distance of 54 m were fully identifiable. We succeeded to detect and identify eleven hazardous materials of liquid or solid particles, which were either explosives or chemical substances used frequently in chemical plants. We also performed experiments to establish the limit of detection (LOD) of HMX at 10 m, which was estimated to be 6 mg.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.65 no.1
• /
• pp.66-71
• /
• 2016

According to the development of depth sensing devices and depth estimation technology, depth information becomes more important for object detection in computer vision. In terms of recognition rate, pedestrian detection methods have been improved more accurately. However, the methods makes slower detection time. So, many researches have overcome this problem by using GPU. Here, we propose a real-time pedestrian detection algorithm that does not rely on GPU. First, the depth-weighted distance map is used for detecting expected human regions. Next, human detection is performed on the regions. The performance for the proposed approach is evaluated and compared with the previous methods. We show that proposed method can detect human about 7 times faster than conventional ones.