Earticle

This study detected land use change using Landsat satellite image and calculated landscape index to confirm fragmentation of forest land. The land cover classification items were classified into forest land, cropland, grassland, wetlands, and settlements by referring to IPCC guidelines, and the land cover classification was used by object-based classification technique. The landscape structure analysis of land cover change was performed using five landscape index (NumP, MPS, TD, ED, AWMSI). The optimal weights for object-based classification were scale 7, Shape 0.4, Color 0.6, Compactness 0.5, Smoothness 0.5. The change of land cover was increased in the settlement area in 2009 compared to 1989 and decreased in cropland and forest land. The change of landscape index decreased NumP of cropland and forest land in 2009 compared to 1989, but TD and ED increased. This is because the settlement increased, and the cropland and forest land decreased and fragmentation progressed. In conclusion, object analysis is an effective method to confirm change detection and fragmentation, and it is expected to be used for the cause analysis of forest land fragmentation in the future.

This study proposes a mother-slave robot tracking system that identifies the target, predicts its location, and tracks it based on a single image. The proposed system utilizes a Convolutional Neural Network (CNN) for object detection, to identify the target robot. The distance and angle between the robots are then calculated through linear regression analysis, which offers a more efficient and cost-effective solution than traditional methods. The performance of the system was evaluated, resulting in an accuracy of 99.59% for object detection, and an average distance error of 2.04% for the estimated location.

With the growing popularity of smartphones, there has been an increase in the number of accidents involving users walking on stairs or crosswalks while using smartphones. Warning signs and images have been placed around dangerous locations in certain areas. However, this has not been significantly effective in reducing similar incidents. We propose a deep learning method based on object detection using a smartphone. Users are notified of impending detection risks on their smartphone’s screen. Tests demonstrated that our approach could detect stairs and crosswalks with high accuracy (96.7%). The proposed smartphone application includes deep learning network information, hyper-parameter information, and user-experience. Thus, users viewing their smartphone screens while walking can use the proposed solution to prevent accidents. As our knowledge, this is the first approach in the world to warn an imminent danger for smombies using a deep learning-based method.

Vision, Radar, and LiDAR sensors are widely used for autonomous vehicle perception technology. Especially object detection and classification are primarily dependent on vision sensors. However, under poor lighting conditions, dazzling sunlight, or bad weather an object might be difficult to be identified with general vision sensors. In this paper, we propose a sensor fusion system that combines a thermal infrared camera and a LiDAR sensor that can reliably detect and identify objects even in environments with poor visibility, such as day or night. The proposed method obtains the external parameters of the two sensors by designing and manufacturing a 3D calibration target to externally calibrate the thermal infrared camera and the LiDAR sensor. To verify the performance, experiments were conducted in day and night environments. The proposed sensor system and fusion algorithm show that it can reliably detect and identify objects even in environments with poor visibility, such as day or night.

Recent knowledge distillation (KD) for 3D object detection often involves costly LiDAR or multi-camera data. We focus on monocular camera-based 3D detectors, where missing 3D cues cause large feature gaps. To address this, we propose region-aware KD, aligning object features by matching their scales and pyramid levels. We introduce a probabilistic distribution to weigh region importance. Applied to MonoRCNN++ and MonoDETR on the KITTI and Waymo dataset, our approach achieves reduced complexity and strong performance with a lightweight backbone. Compared to recent KD methods, ours excels in both effectiveness and efficiency.

Rumination in cattle is closely related to their health, which makes the automatic monitoring of rumination an important part of smart pasture operations. However, manual monitoring of cattle rumination is laborious and wearable sensors are often harmful to animals. Thus, we propose a computer vision-based method to automatically identify multi-object cattle rumination, and to calculate the rumination time and number of chews for each cow. The heads of the cattle in the video were initially tracked with a multi-object tracking algorithm, which combined the You Only Look Once (YOLO) algorithm with the kernelized correlation filter (KCF). Images of the head of each cow were saved at a fixed size, and numbered. Then, a rumination recognition algorithm was constructed with parameters obtained using the frame difference method, and rumination time and number of chews were calculated. The rumination recognition algorithm was used to analyze the head image of each cow to automatically detect multi-object cattle rumination. To verify the feasibility of this method, the algorithm was tested on multi-object cattle rumination videos, and the results were compared with the results produced by human observation. The experimental results showed that the average error in rumination time was 5.902% and the average error in the number of chews was 8.126%. The rumination identification and calculation of rumination information only need to be performed by computers automatically with no manual intervention. It could provide a new contactless rumination identification method for multi-cattle, which provided technical support for smart pasture.

Objective: To develop and verify the usability of a cognitive rehabilitation system with diverse cognitive functional levels based on tangible objects for the elderly population. Methods: A study was conducted to investigate the system's strengths and weaknesses by upgrading it with responses from two groups of 15 patients and 4 occupational therapists. After undergoing three forms of training - regarding executive function, memory, and concentration for a total of 20?30 min, the participants were asked to answer a structured questionnaire about contents of the three forms of training, hardware including the tablet PC functioning as a CPU and display media and tangible objects, and satisfaction of experiential usage of the system. Results: Both groups responded that the most interesting training area was executive function while the least interesting was concentration. Six participants reported that the size of the screen of the tablet PC was inappropriate, and five responded that the size of the tool was inappropriate. All therapists and 40% of the patients responded that they were satisfied with this system. Conclusion: This system’s features include easy manipulation of tangible tools for performing training tasks, easy selection of and training in cognitive areas based on users’ needs, and automatic adjustment of difficulty level based on users’ performance. The training environment was designed to be similar to the natural environment by using tangible objects in both hands as input devices for the system, and the system was considered as an alternative to the lack of community cognitive rehabilitation specialists.

In this paper, an adaptive algorithm is proposed in streaming MPEG-4 contents with fluctuating resource amount such as throughput of network conditions. MPEG-4 is the international standard for audiovisual presentation which is composed of object based media streams. The proposed technique provides the media stream corresponding an object with multiple media streams with different qualities and bit rate in order to support object based scalability to the MPEG-4 content. In addition, making the object streams adaptable, a feasible stream set selected from the multiple streams for transmission with optimal quality in the form of the current status.

Mobile devices, such as PDAs, portable multimedia players, are more likely to encompass large storage devices with prevalance of high-quality multimedia contents. This paper proposes an object-based multimedia contents storage architecture that employs the object-based storage device model and the iSCSI protocol. It also provides a multimedia content player that operates directly with the proposed storage architecture. We implement both the proposed storage architecture and the multimedia content player upon the Linux environment. Performance evaluation by playing MP3 multimedia contents reveals that the proposed storage architecture reduces the total power consumption by 9%, compared with an existing networked storage. This enhancement is mainly contributed to the fact that a large portion of the file system is moved into the object-based multimedia contents storage from the mobile device.

본 연구에서는 게임 영상과 같은 생성된 영상으로부터 물체를 인식하는 심층 학습 기반 모델의 성능을 향상 시키는 방법을 제시한다. 특히, 실제 영상으로 훈련된 물체 인식 모델에 대해서 게임 영상으로 추가 훈련을 수 행함으로써 물체 인식 성능이 향상됨을 검증한다. 본 연구에서는 심층 학습 기반의 물체 인식 모델들 중에서 가장 널리 사용되는 YoloV2 모델을 이용한다. 이 모델에 대해서 8 종류의 다양한 게임에서 샘플링한 160장의 게임 영상을 적용해서 물체 인식 모델을 다시 훈련하고, IoU와 정확도를 측정해서 본 연구에서 주장하는 게임 영상을 이용한 훈련이 효과적임을 입증한다.

We present a framework that improves the performance of deep learning-based object detection model for generated images including game scenes. In particular, we aim to verify that the additional training using images sampled from game scenes can improve the performance of the object detection model, which was pre-trained using photographs. Among the various object detection schemes including Yolo V1, Yolo V2 and SSD, we employ YoloV2 model, which is one of the most widely used deep learning-based object detection model. YoloV2 model is pretrained using diverse photographs. This model is further trained through 160 game scene images sampled from eight different kinds of games. We select the games that range from realistic scenes and highly deformed scenes. We measure IoU (intersection over union) and accuracy using this model. The comparison between our re-trained model and the original model demonstrates the effectiveness of our strategy.

This study was performed to construct tree species classification map according to three information types (spectral information, texture information, and spectral and texture information) by altitude (30 m, 60 m, 90 m) using the unmanned aerial vehicle images and the object-based classification method, and to evaluate the concordance rate through field survey data. The object-based, optimal weighted values by altitude were 176 for 30 m images, 111 for 60 m images, and 108 for 90 m images in the case of Scale while 0.4/0.6, 0.5/0.5, in the case of the shape/color and compactness/smoothness respectively regardless of the altitude. The overall accuracy according to the type of information by altitude, the information on spectral and texture information was about 88% in the case of 30 m and the spectral information was about 98% and about 86% in the case of 60 m and 90 m respectively showing the highest rates. The concordance rate with the field survey data per tree species was the highest with about 92% in the case of Pinus densiflora at 30 m, about 100% in the case of Prunus sargentii Rehder tree at 60 m, and about 89% in the case of Robinia pseudoacacia L. at 90 m.

Background/Objectives Deep learning which main technology in AI has high growth with being applied to field of speech recognition and Image classification. Especially, Deep learning technology in the field of Image classification is being applied as a core technology to Self-driving and crime prevention monitoring system that is recently emerging as the future industry. Methods/Statistical analysis: Various algorithm which is improved and developed CNN being able to do image process is suggested as Deep learning model in image recognition field. In this paper, we introduce various object detection algorithm including CNN. And explore most representative algorithms just R-CNN, Fast R-CNN, Faster R-CNN and difference between versions of YOLO devised to detect and track in real time. Findings: This paper evaluates deep learning algorithm’s performance by comparative analysis about mAP (mean average precision) and FPS (frames per second). In result of performance evaluation, YOLO algorithm is confirmed as that It shows excellent result in speed that detects and recognizes object and accuracy in real time system environment. Finally, we search cases in field of autonomous driving and access control system and home anti-crime system. Improvements/Applications: In this research, we can understand object detection algorithm among speech recognition technologies and proper field in each algorithm, apply security service based on image, recommend proper algorithm in various environment just like autonomous driving and security work, etc.

In this study, regions infected by pine wilt disease were extracted by using object-based classification method (OB-infected region), and the characteristics of special distribution about OB-infected region were figured out. Scale 24, Shape 0.1, Color 0.9, Compactness 0.5, and Smoothness 0.5 was selected as the objected-based, optimal weighted value of OB-infected region classification. The total accuracy of classification was high with 99% and Kappa coefficient was also high with 0.97. The area of OB-infected region was approximately 90 ha, 16% of the total area. The OB-infected region in Age class V and VI was intensively distributed with 97% of the total. Also, The OB-infected region in Middle and Large DBH class was intensively distributed with 99% of the total. In terms of the topographic characteristics of OB-infected region, the damages occurred approximately 86% below the altitude of 200 m, and occurred 91% with a slope less than 10 degree. The damage occurred a lot in low hilly mountain and undulating slope. In addition, the accessibility to road and residential area from OB-infected region was less than 300 m in large part. Overall, it was figured out that artificial effect is stronger than natural effect with regard to the spread of pine wilt disease.

생산 시스템은 일반적으로 규모가 크고 복잡하며 동시 발생적인 특성을 가지는 경우가 많다. 이러한 특징은 시스템의 행태를 사전에 분석하고 시뮬레이션하기 위한 모델링에 많은 어려움을 가져오게 한다. 본 논문은 이러한 어려움을 해결하기 위하여 객체지향 페트리네트를 이용한 생산시스템 모델링 및 분석 도구를 제사한 것이다. 기본적인 구조는 페트리네트를 객체의 개념으로 구성하는 것이다. 객체의 개념으로 페트리네트를 구성하고 시스템의 행태는 객체간의 메시지 교환과 객체내의 행태로 표현된다. 시스템 분석에 있어서는 객체간의 메시지 전달관계와 객체내의 행태 분석 등이 가능하다. 대상 시스템이 객체의 클래스 개념으로 구성되기 때문에 복잡도가 많이 감소될 수 있다. 특히 본 논문에서는 상속성의 개념을 객체 지향 페트리네트에서 어떻게 구현할 것인가에 대한 방법과 분석 방법이 제시되었다.

In the content-based image search properties, form information is simple because only the contours of objects are available, and although it can effectively extract the characteristics of the objects, it is sensitive to external noise. The radial distortion, one of these noises, is most prominent in the eyewear and, due to the structural characteristics of the imaging equipment, radiative distortion occurs in almost all imaging equipment. It is very important to determine the similarity of the objects in the images in which these distortions occurred to the actual objects. In order to improve this problem, we propose a strong image search technique for formative noise and radiative distortion using regularization phase angles and moments. Through simulation using Wang DB, the proposed algorithm proved excellent performance for radiation distortion that occurs in general. In addition, a system optimized for database can be implemented by making appropriate changes to the threshold values, enabling image retrieval with the desired level of confidence in various systems. The algorithm proposed in this paper is expected to be utilized as an optimal imaging system by extracting morphological form information of multimedia data.

This paper presents an improved approach to generate pseudo labels for unlabeled dataset. To properly train a network, large amount of dataset is required. The publicly available datasets are often not large enough or versatile. Although we can acquire a great deal of images from the internet, those images are not labeled. Conventionally, the generation of ground truth labels requires human effort which is very expensive and time-consuming. Recently, existing object detectors are being employed to automate the generation of labels, called pseudo labels. Such pseudo labels have poor accuracy, since most of the object detectors employ simplistic confidence thresholding, which tends to discard even good labels. This paper proposes an enhanced pseudo labeling technique that selects the predicted labels using a bi-directional tracking method instead of simplistic confidence thresholding. The proposed technique can recover many predicted labels that are actual good labels but would have been discarded due to their poor confidence. Our method can produce pseudo labels for new training dataset with higher accuracy than conventional pseudo labeling techniques, thus offering better training accuracy for object detector CNN models.