• The Journal of Korea Robotics Society
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• v.16 no.1
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• pp.49-55
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• 2021

Recently, a smart farm technology is drawing attention as an alternative to the decline of farm labor population problems due to the aging society. Especially, there is an increasing demand for automatic harvesting system that can be commercialized in the market. Pre-harvest crop detection is the most important issue for the harvesting robot system in a real-world environment. In this paper, we proposed a real-time tomato instance tracking algorithm by using deep learning and probability models. In general, It is hard to keep track of the same tomato instance between successive frames, because the tomato growing environment is disturbed by the change of lighting condition and a background clutter without a stochastic approach. Therefore, this work suggests that individual tomato object detection for each frame is conducted by YOLOv3 model, and the continuous instance tracking between frames is performed by Kalman filter and probability model. We have verified the performance of the proposed method, an experiment was shown a good result in real-world test data.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.275-284
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• 2023

Land monitoring involves systematically understanding changes in land use, leveraging spatial information such as satellite imagery and aerial photographs. Recently, the integration of deep learning technologies, notably object detection and semantic segmentation, into land monitoring has spurred active research. This study developed a web service to facilitate such integrations, allowing users to analyze aerial and drone images using CNN models. The web service architecture comprises AI, WEB/WAS, and DB servers and employs three primary deep learning models: DeepLab V3, YOLO, and Rotated Mask R-CNN. Specifically, YOLO offers rapid detection capabilities, Rotated Mask R-CNN excels in detecting rotated objects, while DeepLab V3 provides pixel-wise image classification. The performance of these models fluctuates depending on the quantity and quality of the training data. Anticipated to be integrated into the LX Corporation's operational network and the Land-XI system, this service is expected to enhance the accuracy and efficiency of land monitoring.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.3
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• pp.97-107
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• 2017

In the field of automatic door, the infrared rays and microwave sensor are much used as the important components in charge of the motor's operation control of open and close through the incoming signal of object recognition. In case of existing system that the sensor of the infrared rays and microwave are applied to the automatic door, there are many malfunctions by the infrared rays and visible rays of the sun. Because the automatic doors are usually installed outside of building in state of exposure. The environmental change by temperature difference occurs the noise of object recognition detection signal. With this problem, the hardware fault that the detection sensor is unable to follow the object moving rapidly within detection area makes the sensing blind spot. This fault should be improved as soon as possible. Because It influences safety of passengers who use the automatic doors. This paper conducted an experiment to improve the detection area by installing extra ultrasonic sensor besides existing detection sensor. So, this paper realize the computing circuit and detection algorithm which can correctly and rapidly process the access route of objects moving fast and the location area of fixed obstacles by applying detection and advantages of ultrasonic signal to the automatic doors. With this, It is proved that the automatic door applying ultrasonic sensor is improved detection area of blind spot sensing through field test and improvement plan is proposed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.11a
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• pp.97-101
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• 2004

In this paper, we study the fishery detection system for protection of an aquamlture farm. The FDS(fishery detection system) will be recognize a robbing vessel with real time and variance the position of aquaculture farm. We try to develop the F-AIS(Fishery Automatic Identification System) which am be detect approaching object to aquaculture farm and distinguish our fishing boot from thief vessel. The F-AIS with low price and wideband responsibility am be adopt to the FDS, i. e. the identification for a small-sized fishing boots.

• Journal of the Semiconductor & Display Technology
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• v.20 no.1
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• pp.94-98
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• 2021

As the world's population grows, the food industry becomes increasingly important. Among them, agriculture is an industry that produces stocks of people all over the world, which is very important food industry. Despite the growing importance of agriculture, however, a large number of crops are lost every year due to pests and malnutrition. So, we propose a plant anomaly detection system for managing crops incorporating deep learning and drones with various possibilities. In this paper, we develop a system that analyzes images taken by drones and GPS of the drone's movement path and visually displays them on a map. Our system detects plant anomalies with 97% accuracy. The system is expected to enable efficient crop management at low cost.

• Journal of information and communication convergence engineering
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• v.2 no.1
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• pp.52-57
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• 2004

In this paper, we propose a panorama-based object tracking scheme for wide-view surveillance systems that can detect and track moving objects with a pan-tilt camera. A dynamic mosaic of the background is progressively integrated in a single image using the camera motion information. For the camera motion estimation, we calculate affine motion parameters for each frame sequentially with respect to its previous frame. The camera motion is robustly estimated on the background by discriminating between background and foreground regions. The modified block-based motion estimation is used to separate the background region. Each moving object is segmented by image subtraction from the mosaic background. The proposed tracking system has demonstrated good performance for several test video sequences.

• Journal of Sensor Science and Technology
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• v.14 no.3
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• pp.198-205
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• 2005

Recently, the systems using multiple sensors such as magnetic, acoustic and pressure sensor are used for detection of underwater objects or vehicles. Those systems have difficulty of maintenance and repair because they operate underwater. Thus, this paper describes a hybrid detection system with long term operating reliability. This has a multi-signal transmission structure to have a high reliability. First, a signal transmission & receiving part, which transfers data from underwater sensors to land and receive control message from land through optical cable, has 4 multi-path. Second, the nodes for signal transmission are connected dually each other with single-hop construction and sensors are connected to a couple of neighboring nodes. This enables the output signal to transmit from a node to the next node and the next but one node together. Also, the signal from a sensor can be transmitted to two nodes at the same time. Therefore, the system with this construction has high reliability in long term operation because it makes possible to transmit sensor data to another node which works normally although a transmission node or cable in system have some faults.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.382-386
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• 2011

It has been required to develop a defect detection system to perform defect detection capabilities after the bonding process in the production of RFID tags. However, we are difficult to design a system with understanding the characteristics of RFID tags and design concepts. Also we are difficult to modify even minor changes in features. In this paper, we design the defect RFID detection system using UML and object-oriented design techniques. We suggest the method for apply the UML Diagram to LabVIEW OOP and the technique for redesign the effect detection system's changes.

• Korean Journal of Materials Research
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• v.31 no.12
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• pp.719-726
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• 2021

Through the process of chemical vapor deposition, Tungsten Hexafluoride (WF₆) is widely used by the semiconductor industry to form tungsten films. Tungsten Hexafluoride (WF₆) is produced through manufacturing processes such as pulverization, wet smelting, calcination and reduction of tungsten ores. The manufacturing process of Tungsten Hexafluoride (WF₆) is required thorough quality control to improve productivity. In this paper, a real-time detection system for oxidation defects that occur in the manufacturing process of Tungsten Hexafluoride (WF₆) is proposed. The proposed system is implemented by applying YOLOv5 based on Convolutional Neural Network (CNN); it is expected to enable more stable management than existing management, which relies on skilled workers. The implementation method of the proposed system and the results of performance comparison are presented to prove the feasibility of the method for improving the efficiency of the WF₆ manufacturing process in this paper. The proposed system applying YOLOv5s, which is the most suitable material in the actual production environment, demonstrates high accuracy (mAP@0.5 99.4 %) and real-time detection speed (FPS 46).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.3
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• pp.399-407
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• 2019

In Korea, the construction industry has been known to have the highest risk of safety accidents compared to other industries. Therefore, in order to improve safety in the construction industry, several researches have been carried out from the past. This study aims at improving safety of labors in construction site by constructing an effective automatic safety helmet detection system using object detection algorithm based on image data of construction field. Deep learning was conducted using Region-based Fully Convolutional Network (R-FCN) which is one of the object detection algorithms based on Convolutional Neural Network (CNN) with Transfer Learning technique. Learning was conducted with 1089 images including human and safety helmet collected from ImageNet and the mean Average Precision (mAP) of the human and the safety helmet was measured as 0.86 and 0.83, respectively.