• Journal of IKEEE
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• v.27 no.4
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• pp.439-449
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• 2023

In an era marked by the increasing use of drones and the growing demand for indoor surveillance, the development of a robust application for detecting and tracking both drones and humans within indoor spaces becomes imperative. This study presents an innovative application that uses FMCW radar to detect human and drone motions from the cloud point. At the outset, the DBSCAN (Density-based Spatial Clustering of Applications with Noise) algorithm is utilized to categorize cloud points into distinct groups, each representing the objects present in the tracking area. Notably, this algorithm demonstrates remarkable efficiency, particularly in clustering drone point clouds, achieving an impressive accuracy of up to 92.8%. Subsequently, the clusters are discerned and classified into either humans or drones by employing a deep learning model. A trio of models, including Deep Neural Network (DNN), Residual Network (ResNet), and Long Short-Term Memory (LSTM), are applied, and the outcomes reveal that the ResNet model achieves the highest accuracy. It attains an impressive 98.62% accuracy for identifying drone clusters and a noteworthy 96.75% accuracy for human clusters.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1109-1124
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• 2022

This study proposes a marine debris monitoring methods using satellite and drone multispectral images. A multi-layer perceptron (MLP) model was applied to detect marine debris using Sentinel-2 satellite image. And for the detection of marine debris using drone multispectral images, performance evaluation and comparison of U-Net, DeepLabv3+ (ResNet50) and DeepLabv3+ (Inceptionv3) among deep learning models were performed (mIoU 0.68). As a result of marine debris detection using satellite image, the F1-Score was 0.97. Marine debris detection using drone multispectral images was performed on vegetative debris and plastics. As a result of detection, when DeepLabv3+ (Inceptionv3) was used, the most model accuracy, mean intersection over union (mIoU), was 0.68. Vegetative debris showed an F1-Score of 0.93 and IoU of 0.86, while plastics showed low performance with an F1-Score of 0.5 and IoU of 0.33. However, the F1-Score of the spectral index applied to generate plastic mask images was 0.81, which was higher than the plastics detection performance of DeepLabv3+ (Inceptionv3), and it was confirmed that plastics monitoring using the spectral index was possible. The marine debris monitoring technique proposed in this study can be used to establish a plan for marine debris collection and treatment as well as to provide quantitative data on marine debris generation.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.4
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• pp.217-222
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• 2017

Recent studies for working robot in dangerous places have been carried out on large unmanned ground vehicles or 4-legged robots with the advantage of long working time, but it is difficult to apply in practical dangerous fields which require the real-time system with high locomotion and capability of delicate working. This research shows the collaborated docking system of drone and ground vehicles which combines image processing algorithm and laser sensors for effective detection of docking markers, and is finally capable of moving a long distance and doing very delicate works. We proposed the docking system of drone and ground vehicles with sensor fusion which also suggests two template matching methods appropriate for this application. The system showed 95% docking success rate in 50 docking attempts.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.619-627
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• 2021

Recent advance in low cost and light-weight drones has extended their application areas in both military and private sectors. Accordingly surveillance program against unfriendly drones has become an important issue. Drone detection and classification technique has long been emphasized in order to prevent attacks or accidents by commercial drones in urban areas. Most commercial drones have small sizes and low reflection and hence typical sensors that use acoustic, infrared, or radar signals exhibit limited performances. Recently, artificial intelligence algorithm has been actively exploited to enhance radar image identification performance. In this paper, we adopt machined learning algorithm for high resolution radar imaging in drone detection and classification applications. For this purpose, simulation is carried out against commercial drone models and compared with experimental data obtained through high resolution radar field test.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1737-1741
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• 2022

Active fire monitoring using high-resolution drone images and deep learning technologies is now an initial stage and requires various approaches for research and development. This letter examined the detection of active fire objects using You Look Only Once Version 7 (YOLOv7), a state-of-the-art (SOTA) model that has rarely been used in fire detection with drone images. Our experiments showed a better performance than the previous works in terms of multiple quantitative measures. The proposed method can be applied to continuous monitoring of wide areas, with an integration of additional development of new technologies.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.397-403
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• 2024

North Korea's rapidly advancing drone development and operational capabilities have become a significant threat to South Korea's security. The drone incursions by North Korea in 2014, 2017, and 2022 demonstrate the technological advancement and provocative potential of North Korean drones. This study aims to closely analyze the military threats posed by North Korean drones and seek effective countermeasures. The research examines the development level of North Korean drone technology, its military applications, the characteristics and patterns of recent drone incursions, the adequacy and limitations of South Korea's current response systems, and future countermeasures. For this purpose, domestic and international research literature and media reports were reviewed, and specific North Korean drone incursion cases were analyzed. The results indicate that North Korea's small drones possess technological features such as small size, low altitude, low-speed flight, long-duration flight, and reconnaissance equipment. These drones pose threats that can be utilized for reconnaissance, surveillance, surprise attacks, and terrorism. Additionally, South Korea's current response systems reveal limitations such as inadequate detection and identification capabilities, low interception success rates, lack of an integrated response system, and insufficient specialized personnel and equipment. Therefore, this study suggests various technical, policy, and international cooperative countermeasures, including the development of drone detection and identification technologies, the utilization of diverse drone neutralization technologies, the establishment of legal and institutional foundations, the construction of a cooperative framework among relevant agencies, and the strengthening of international cooperation. The study particularly emphasizes the importance of raising awareness of the North Korean drone threat across South Korean society and unifying national efforts to respond to these threats.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.21-31
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• 2018

The result of examination of diagnostic method, which is composed of a combination of a thermal camera and a drone that visually shows the temperature of the object by detecting the infrared rays, for detecting the leakage of earth dam was driven in this research. The drone infrared thermography method was suggested to precise safety diagnosis through direct comparing the two method results of electrical resistivity survey and thermal image survey. The important advantage of the thermal leakage detection method was the simplicity of the application, the quickness of the results, and the effectiveness of the work in combination with the existing diagnosis method.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.470-477
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• 2022

EOTS for drones is showing another aspect of market expansion in detection and recognition areas previously occupied by artificial satellites. The two-axis EOTS for drones controls the vibration or disturbance caused by the drone during the mission so that EOTS can accurately recognize the goal. Vibration generated by drones is transmitted to EOTS. Therefore, it is essential to develop a stabilization controller that attenuates vibrations transmitted from drones so that EOTS can maintain the viewing angle. Therefore, it is necessary to standardize drone disturbance and secure the performance of EOTS disturbance attenuation controller optimized for disturbance level through this. In this paper, a method of standardizing drone disturbance applied to EOTS is studied, through which EOTS controller simulation is performed and stabilization controller shape is selected and designed.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.261-262
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• 2023

Drone is a suitable equipment for capturing images of cracks at construction sites based on its efficient mobility and high-resolution image acquisition capabilities. In this study, drone was used to acquire indoor construction sites framework images and deep learning technology was applied to detect cracks and measure width, and size. Finally, the usability of the process was verified based on the indoor crack monitoring system.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.3
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• pp.107-112
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• 2018

In this paper, optical flow based keypoint detection and tracking technique is proposed for the collaboration between flying drone with vision system and ground robots. There are many challenging problems in target detection research using moving vision system, so we combined the improved FAST algorithm and Lucas-Kanade method for adopting the better techniques in each feature detection and optical flow motion tracking, which results in 40% higher in processing speed than previous works. Also, proposed image binarization method which is appropriate for the given marker helped to improve the marker detection accuracy. We also studied how to optimize the embedded system which is operating complex computations for intelligent functions in a very limited resources while maintaining the drone's present weight and moving speed. In a future works, we are aiming to develop collaborating smarter robots by using the techniques of learning and recognizing targets even in a complex background.