• Aerospace Engineering and Technology
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• v.3 no.1
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• pp.242-250
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• 2004

This paper describes the detection method of wire-like obstacles using millimeter-wave radar system. Passive sensor like CCD camera can be used for the detection of high power electric cables on the hills or mountains and it can give very good quality of obstacle target information. But this system is very limited to use by bad weather condition. The detection capability for different diameters of wire targets using millimeter radar system have been accomplished. To simulate the target on the moving helicopter, rotating targets are used with fixed radar system. In the experiment 11mm, 16mm and 22mm diameter of wires have been detected in single, two and three wires in one position. The detected signal from single wire was very clear on gray level image. Three wires placed very closely together could be recognized in range, cross range image plane. For two and three wires, blur effect due to mutual scattering effect is observed.

• Transactions on Semiconductor Engineering
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• v.2 no.1
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• pp.17-20
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• 2024

This paper presents a novel lightweight object detection model tailored for low-powered edge devices, addressing the limitations of traditional resource-intensive computer vision models. Our proposed detector, inspired by the Single Shot Detector (SSD), employs a compact yet robust network design. Crucially, it integrates an 'enhancer block' that significantly boosts its efficiency in detecting smaller objects. The model comprises two primary components: the Light_Block for efficient feature extraction using Depth-wise and Pointwise Convolution layers, and the Enhancer_Block for enhanced detection of tiny objects. Trained from scratch on the Udacity Annotated Dataset with image dimensions of 300x480, our model eschews the need for pre-trained classification weights. Weighing only 5.5MB with approximately 0.43M parameters, our detector achieved a mean average precision (mAP) of 27.7% and processed at 140 FPS, outperforming conventional models in both precision and efficiency. This research underscores the potential of lightweight designs in advancing object detection for edge devices without compromising accuracy.

• Journal of the Korea Society of Computer and Information
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• v.29 no.11
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• pp.153-161
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• 2024

In this paper, we address the limitation of single-channel infrared (IR) images, which are difficult to directly apply to RGB-based detection models. Previously, a single channel was often replicated into three channels; however, this approach may limit detection performance due to information redundancy. To overcome this limitation, we propose a method that replicates the single-channel IR image into three channels, with each channel processed using different preprocessing techniques, such as CLAHE (Contrast Limited Adaptive Histogram Equalization), Laplacian Filter, and Top-hat transform, to improve detection performance. In this study, we utilized the RT-DETRv2 detection model and the Anti-UAV300 dataset, using IR images sampled at 10-frame intervals for our experiments. By evaluating the effects of each preprocessing technique and deriving the optimal configuration, our method achieved a 2.2% improvement in mean Average Precision (mAP) over conventional methods. This confirms that our method enhances performance over simple replication, presenting a novel approach to improving object detection performance in IR imaging, with promising applications across various fields, particularly in disaster situations where infrared cameras are utilized, as well as in nighttime surveillance and reconnaissance.

• Korean Journal of Remote Sensing
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• v.29 no.6
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• pp.645-655
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• 2013

As one of segmentation techniques for Synthetic Aperture Radar (SAR) image with oil spill, we applied a bimodal histogram method to discriminate oil pixels from non-oil pixels. The threshold of each moving window was objectively determined using the two peaks in the histogram distribution of backscattering coefficients from ENVISAT ASAR image. To reduce the effect of wind speed on oil spill detection, we selected ASAR image which satisfied a limit of wind speeds for successful detection. Overall, a commonly used adaptive threshold method has been applied with a subjectively-determined single threshold. In contrast, the bimodal histogram method utilized herein produces a variety of thresholds objectively for each moving window by considering the characteristics of statistical distribution of backscattering coefficients. Comparison between the two methods revealed that the bimodal histogram method exhibited no significant difference in terms of performance when compared to the adaptive threshold method, except for around the edges of dark oil spots. Thus, we anticipate that the objective method based on the bimodality of oil slicks may also be applicable to the detection of oil spills from other SAR imagery.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3844-3853
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• 2023

Although radiation and chemotherapy methods for cancer therapy have advanced significantly, surgical resection is still recommended for most cancers. Therefore, intraoperative imaging studies have emerged as a surgical tool for identifying tumor margins. Intraoperative imaging has been examined using conventional imaging devices, such as optical near-infrared probes, gamma probes, and ultrasound devices. However, each modality has its limitations, such as depth penetration and spatial resolution. To overcome these limitations, hybrid imaging modalities and tracer studies are being developed. In a previous study, a multi-modal laparoscope with silicon photo-multiplier (SiPM)-based gamma detection acquired a 1 s interval gamma image. However, improvements in the near-infrared fluorophore (NIRF) signal intensity and gamma image central defects are needed to further evaluate the usefulness of multi-modal systems. In this study, an attempt was made to change the NIRF image acquisition method and the SiPM-based gamma detector to improve the source detection ability and reduce the image acquisition time. The performance of the multi-modal system using a complementary metal oxide semiconductor and modified SiPM gamma detector was evaluated in a phantom test. In future studies, a multi-modal system will be further optimized for pilot preclinical studies.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.177-182
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• 2004

This study presents the application of aerial photographs and KOMPSAT-1 Electro-Optical Camera(EOC) imagery in detecting the change of an urban area that has been rapidly growing. For the study, we used multi-time images which were acquired by two different sensors. For all of the images, the coordinate reference system and scale were first made identical through the 1st and 2nd geometric corrections and then image resampling were carried out to spatial resolution of 7m to detect changes under the same conditions. The Image Differencing was employed as a change detection technique. It was confirmed to be able to detect the changes of terrestrial surface like building, structure and road features from aerial photos and KOMPSAT EOC images with single band. The changes could be detected to some extent with the images acquired from different kinds of sensors as well as the same kinds of sensors.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.1
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• pp.72-79
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• 2013

Different scanning methods can be used to detect targets of interest in an image. In this paper, four scanning methods, generalized raster scanning, radial scanning, corner scanning, and random scanning, are considered for the evaluation of their scanning performances. The scanning performance is defined here as the ratio of the average scanning area required to detect a single target to the whole image area. Analytic expressions for the performance of each scanning method are derived. Computational results are given to illustrate the usage and validity of the expressions for the performance comparison.

• Proceedings of the Korean Fiber Society Conference
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• 2003.10a
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• pp.100-100
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• 2003

An image processing algorithm has been developed in order to analyze the nanofiber web images obtained from a high magnification microscope. It has been known that precise pore detection on thick webs is extremely difficult mainly due to lack of light uniformity, difficulty of fine focusing and translucency of nanofiber web. The pore detection algorithm developed has been found to show excellent performance in characterizing the porous structure, thus being a promising tool for on-line quality control system under mass production. Since the images obtained from an optical microscope represent only web surface, a scale factor has been introduced to estimate the web structure as a whole. Resulting web structures have been compared to those by mercury porosimetry, especially in pore size distribution. It has been shown that those two structures have a strong correlation, indicating that scaling of a single layer web structure can be an effective way of estimating the structure of thick fiber webs.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.601-604
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• 1994

This paper describes an effective method to estimate a position of an automous vehicle equipped with a single CCD-camera along indoor passageways. Using the sequential image data from the self-movement of the vehicle, the position is estimated by integrating the approximated motion parameters. The detection of the yaw angle that is one of the motion parameter is difficult in general, e.g. slip or error for noise, therefore the different detection is presented, which is, without shaft encoders, based on a projection function for 2D-image data and a cross-correlation function so as to be robust for noise. The approximated geometric function to estimate the position is used to reduce the computational effort. To verify the effectiveness of the method, the analysis and the computational results are shown through the simulations. Furthermore, the experimental results by using the test vehicle for the real indoor passageway are shown.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.2
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• pp.35-41
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• 2018

The analysis of plankton species distribution in sea or fresh water is very important in preserving marine ecosystem health. Since manual analysis is infeasible, many automatic approaches were proposed. They usually use images from in situ towed underwater imaging sensor or specially designed, lab mounted microscopic imaging system. Normally they assume that only single plankton is present in an image so that, if there is a clumping among multiple plankton of same species (homogeneous clumping) or if there are multiple plankton of different species scattered in an image (heterogeneous interspersion), they have a difficulty in recognition. In this work, we propose a deep learning based method that can detect and recognize individual plankton in images with homogeneous clumping, heterogeneous interspersion, or combination of both.