• Smart Media Journal
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• v.11 no.4
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• pp.46-55
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• 2022

It is very important to first detect and remove defective fruits with scratches or bruised areas in the automatic fruit quality screening system. This paper proposes a method of detecting defective areas in fruits using the latest artificial intelligence technology, the Yolo V4 deep learning model in order to overcome the limitations of the method of detecting fruit's defective areas using the existing image processing techniques. In this study, a total of 2,400 defective fruits, including 1,000 defective apples and 1,400 defective fruits with scratch or decayed areas, were learned using the Yolo V4 deep learning model and experiments were conducted to detect defective areas. As a result of the performance test, the precision of apples is 0.80, recall is 0.76, IoU is 69.92% and mAP is 65.27%. The precision of pears is 0.86, recall is 0.81, IoU is 70.54% and mAP is 68.75%. The method proposed in this study can dramatically improve the performance of the existing automatic fruit quality screening system by accurately selecting fruits with defective areas in real time rather than using the existing image processing techniques.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.2
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• pp.63-70
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• 2022

Although 2D Object detection has been largely improved in the past years with the advance of deep learning methods and the use of large labeled image datasets, 3D object detection from 2D imagery is a challenging problem in a variety of applications such as robotics, due to the lack of data and diversity of appearances and shapes of objects within a category. Google has just announced the launch of Objectron that has a novel data pipeline using mobile augmented reality session data. However, it also is corresponding to 2D-driven 3D object detection technique. This study explores more mature 2D object detection method, and applies its 2D projection to Objectron 3D lifting system. Most object detection methods use bounding boxes to encode and represent the object shape and location. In this work, we explore a stochastic representation of object regions using Gaussian distributions. We also present a similarity measure for the Gaussian distributions based on the Hellinger Distance, which can be viewed as a stochastic Intersection-over-Union. Our experimental results show that the proposed Gaussian representations are closer to annotated segmentation masks in available datasets. Thus, less accuracy problem that is one of several limitations of Objectron can be relaxed.

• Journal of IKEEE
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• v.26 no.2
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• pp.160-168
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• 2022

In this paper, we propose a 3D mesh reconstruction method from a single image using deep learning and a sphere shape transformation method. The proposed method has the following originality that is different from the existing method. First, the position of the vertex of the sphere is modified to be very similar to the 3D point cloud of an object through a deep learning network, unlike the existing method of building edges or faces by connecting nearby points. Because 3D point cloud is used, less memory is required and faster operation is possible because only addition operation is performed between offset value at the vertices of the sphere. Second, the 3D mesh is reconstructed by covering the surface information of the sphere on the modified vertices. Even when the distance between the points of the 3D point cloud created by correcting the position of the vertices of the sphere is not constant, it already has the face information of the sphere called face information of the sphere, which indicates whether the points are connected or not, thereby preventing simplification or loss of expression. can do. In order to evaluate the objective reliability of the proposed method, the experiment was conducted in the same way as in the comparative papers using the ShapeNet dataset, which is an open standard dataset. As a result, the IoU value of the method proposed in this paper was 0.581, and the chamfer distance value was It was calculated as 0.212. The higher the IoU value and the lower the chamfer distance value, the better the results. Therefore, the efficiency of the 3D mesh reconstruction was demonstrated compared to the methods published in other papers.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1329-1341
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• 2022

Destroying the marine environment and marine ecosystem and causing marine accidents, marine debris is generated every year, and among them, submerged marine debris is difficult to identify and collect because it is on the seabed. Therefore, deep-learning-based semantic segmentation was experimented on waste fish nets and waste ropes using underwater images to identify efficient collection and distribution. For segmentation, a high-resolution network (HRNet), a state-of-the-art deep learning technique, was used, and the performance of each optimizer was compared. In the segmentation result fish net, F1 score=(86.46%, 86.20%, 85.29%), IoU=(76.15%, 75.74%, 74.36%), For the rope F1 score=(80.49%, 80.48%, 77.86%), IoU=(67.35%, 67.33%, 63.75%) in the order of adaptive moment estimation (Adam), Momentum, and stochastic gradient descent (SGD). Adam's results were the highest in both fish net and rope. Through the research results, the evaluation of segmentation performance for each optimizer and the possibility of segmentation of marine debris in the latest deep learning technique were confirmed. Accordingly, it is judged that by applying the latest deep learning technique to the identification of submerged marine debris through underwater images, it will be helpful in estimating the distribution of marine sedimentation debris through more accurate and efficient identification than identification through the naked eye.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.392-399
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• 2023

This paper proposes a deep learning model to determine the region and depth of cabbage cores for robotic automation of the cabbage core removal process during the kimchi manufacturing process. In addition, rather than predicting the depth of the measured cabbage, a model was presented that simultaneously detects and classifies the area by converting it into a discrete class. For deep learning model learning and verification, RGB images of the harvested cabbage 522 were obtained. The core region and depth labeling and data augmentation techniques from the acquired images was processed. MAP, IoU, acuity, sensitivity, specificity, and F1-score were selected to evaluate the performance of the proposed YOLO-v4 deep learning model-based cabbage core area detection and classification model. As a result, the mAP and IoU values were 0.97 and 0.91, respectively, and the acuity and F1-score values were 96.2% and 95.5% for depth classification, respectively. Through the results of this study, it was confirmed that the depth information of cabbage can be classified, and that it can be used in the development of a robot-automation system for the cabbage core removal process in the future.

• Convergence Security Journal
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• v.17 no.3
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• pp.59-64
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• 2017

Korea is experiencing a rapid increase in the number of elderly living alone accompanying the aging society problem, a nd is making efforts to solve the problem through the policy of 'living alone u-care service'. The purpose of this study is to propose a better u-Care service improvement method by applying new technology to improve the user experience of ucare service for the health and safety of the elderly living alone. First, the improvement of u-Care service for elderly livin g alone by applying IoT technology. It provides remote monitoring service using health information data measured through wearable device, and transmits personal health status to medical institution by using personal device such as smart phone, so that remote medical consultation or telemedicine can be connected in the future. Second, improvement of u-Care service through consideration of emotional stability of elderly living alone as well as simple safety and health care through applica tion of emotional service robot technology.It is expected that it will be able to help independent living of one person's elde rly person in the future by providing caring function service to existing u-care service providing service.

• Phonetics and Speech Sciences
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• v.14 no.3
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• pp.11-25
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• 2022

From simultaneous recordings of the external photoglottography, intra-oral air pressure (P_io), airflow and acoustic data from four native Seoul Korean speakers (2 male and 2 female), we have found that the two fricatives are not significantly different in glottal opening peak and airflow peak height either word-initially or word-medially and that the duration of aspiration is significantly reduced in word-medial /s/, compared to those in word-initial /s/, not in /s'/. We have also found that the duration of a high P_io plateau is significantly longer in /s/ than in /s'/ both word-initially and word-medially and that airflow resistance (R=P_io/U) at the onset and offset of a P_io plateau and at the time of airflow peak height is significantly higher in /s'/ than in /s/ across the contexts. However, the differences in P_io peak and F0 are not significant. In addition, the transition time to reach airflow peak height from the offset of a P_io plateau is found to be significantly longer in /s/ than /s'/ in both word-initial and word-medial positions. No significant differences in glottal opening peak and airflow peak height confirm that /s/ is specified as [-spread glottis] like /s'/. As for the other significant differences, we propose that /s/ is [-tense], and /s'/ [+tense].

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.65-73
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• 2023

In this paper, we propose a data augmentation method based on CNN(Convolutional Neural Network) learning for efficiently obtaining concrete crack image datasets. Real concrete crack images are not only difficult to obtain due to their unstructured shape and complex patterns, but also may be exposed to dangerous situations when acquiring data. In this paper, we solve the problem of collecting datasets exposed to such situations efficiently in terms of cost and time by using vector and thickness-based data augmentation techniques. To demonstrate the effectiveness of the proposed method, experiments were conducted in various scenes using U-Net-based crack detection, and the performance was improved in all scenes when measured by IoU accuracy. When the concrete crack data was not augmented, the percentage of incorrect predictions was about 25%, but when the data was augmented by our method, the percentage of incorrect predictions was reduced to 3%.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.11a
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• pp.41-43
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• 2017

본 논문에서는 IoS (Internet of Signage) 환경에서 실감형 데이터 전송을 위한 상황 인지 기반 적응형 전송기법을 적용하고 이를 분석한다. 사이니지 네트워크 환경에서는 임의적인 다중 경로 페이딩, 간섭 등과 같은 왜곡 요소가 존재할 수 있기 때문에 이를 대처하기 위해 무선 송신기에서는 채널 환경에 대한 인지 이후, 그에 따른 MCS (Modulation and Coding Scheme)를 선정 및 안테나 기법을 선정한다. MCS 및 안테나 기법 선정을 위해 무선 송신기에서는 사전에 알고 있는 통계적인 SNR (Signal-to-Noise Ratio) 값을 통해 문턱값을 계산하고, 수신 사이니지로부터 얻은 CSI (Channel State Information)와의 크기 비교를 한다. 시뮬레이션 결과에서는 상황 인지를 기반으로 매 순간 최적의 전송률을 달성할 수 있음을 볼 수 있다.

• Smart Media Journal
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• v.8 no.2
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• pp.39-45
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• 2019

As deep learning has become popular, researches which can help explaining the prediction results also become important. Superpixel based multi-scale combining technique, which provides the advantage of visual pleasing by maintaining the shape of the object, has been recently proposed. Based on the principle of prediction difference, this technique computes the saliency map from the difference between the predicted result excluding the superpixel and the original predicted result. In this paper, we propose a new technique of both excluding and including super pixels. Experimental results show 3.3% improvement in IoU evaluation.