• International Journal of Advanced Culture Technology
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• 제11권1호
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• pp.320-324
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• 2023

As urbanization accelerates and facilities that use energy increase, human life and property damage due to fire is increasing. Therefore, a fire monitoring system capable of quickly detecting a fire is required to reduce economic loss and human damage caused by a fire. In this study, we aim to develop an improved artificial intelligence model that can increase the accuracy of low fire alarms by mixing DeepSORT, which has strengths in object tracking, with the YOLOv5s model. In order to develop a fire detection model that is faster and more accurate than the existing artificial intelligence model, DeepSORT, a technology that complements and extends SORT as one of the most widely used frameworks for object tracking and YOLOv5s model, was selected and a mixed model was used and compared with the YOLOv5s model. As the final research result of this paper, the accuracy of YOLOv5s model was 96.3% and the number of frames per second was 30, and the YOLOv5s_DeepSORT mixed model was 0.9% higher in accuracy than YOLOv5s with an accuracy of 97.2% and number of frames per second: 30.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 추계학술대회
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• pp.175-177
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• 2022

본 논문은 시각장애인이 원하는 목적지까지 버스를 이용하여 이동하는 것을 보조하는 시스템 안에 사용될 수 있는 심층신경망에 따라 성능을 비교하였다. 이 시스템은 GPS, 경로 안내 API를 이용하여 현재 위치에서 목적지까지 이르는 경로를 찾고 안내하는 스마트폰 어플리케이션과 임베디드 보드, 심층신경망, 버스정보 API를 이용해 원하는 버스의 탑승 대기시간을 구하고 도착을 감지하는 정거장 설치형 모듈로 이루어져 있다. 정거장 설치형 모듈에 탑승할 버스 번호를 인식하기 위해서 faster-RCNN, YOLOv4, YOLOv5s 세 가지 심층신경망을 적용했고 최상 정확도와 속도면에서 YOLOv5s 심층신경망이 가장 좋은 성능을 보였다.

• 한국인터넷방송통신학회논문지
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• 제20권6호
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• pp.157-165
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• 2020

뉴-럴 네트워크와 자동운전 데이터 셋을 개발하는 목표중의 하나가 데이터 셋을 분할함에 따라서 움직이는 물체를 검출하는 성능을 개선하는 방법이 있다. 다크넷 (DarkNet) 프레임 워크에 있어서, YOLOv4 네트워크는 Udacity 데이터 셋에서 훈련하는 셋과 검증 셋으로 사용되었다. Udacity 데이터 셋의 7개 비율에 따라서 이 데이터 셋은 훈련 셋, 검증 셋, 테스트 셋을 포함한 3개의 부분 셋으로 나누어진다. K-means++ 알고리즘은 7개 그룹에서 개체 Box 차원 군집화를 수행하기 위해 사용되었다. 훈련을 위한 YOLOv4 네트워크의 슈퍼 파라메타를 조절하여 7개 그룹들에 대하여 최적 모델 파라메타가 각각 구해졌다. 이 모델 파라메타는 각각 7 개 테스트 셋 데이터에 비교하고 검출에 사용되었다. 실험결과에서 YOLOv4 네트워크는 Udacity 데이터 셋에서 트럭, 자동차, 행인으로 표현되는 움직이는 물체에 대하여 대/중/소 물체 검출을 할수 있음을 보여 주었다. 훈련 셋과 검증 셋, 테스트 셋의 비율이 7 ; 1.5 ; 1.5 일 때 최적의 모델 파라메타로서 가장 높은 검출 성능이었다. 그 결과값은, mAP50가 80.89%, mAP75가 47.08%에 달하고, 검출 속도는 10.56 FPS에 달한다.

• 한국ITS학회 논문지
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• 제17권5호
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• pp.173-187
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• 2018

본 논문에서는 주차장 관리 시스템의 한 부분으로 차량 계수를 위한 감시 카메라 시스템의 컴퓨터 비전과 심층 학습 기반 기법을 제안하고자 한다. You Only Look Once 버전 2 (YOLOv2) 탐지기를 적용하고 YOLOv2 기반의 심층 컨볼루션 신경망(CNN)을 다른 아키텍처와 두 가지 모델로 구성하였다. 제안 된 아키텍처의 효과를 Udacity의 자체 운전 차량 데이터 세트를 사용하여 설명하였다. 학습 및 테스트 결과, 자동차, 트럭 및 보행자 탐지 시 원래 구조(YOLOv2)의 경우 47.89%의 mAP를 나타내는 것에 비하여, 제안하는 모델의 경우 64.30 %의 mAP를 달성하여 탐지 정확도가 향상되었음을 증명하였다.

• Journal of Information Processing Systems
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• 제19권5호
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• pp.663-672
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• 2023

Most vehicle detection methods have poor vehicle feature extraction performance at night, and their robustness is reduced; hence, this study proposes a night vehicle detection method based on style transfer image enhancement. First, a style transfer model is constructed using cycle generative adversarial networks (cycleGANs). The daytime data in the BDD100K dataset were converted into nighttime data to form a style dataset. The dataset was then divided using its labels. Finally, based on a YOLOv5s network, a nighttime vehicle image is detected for the reliable recognition of vehicle information in a complex environment. The experimental results of the proposed method based on the BDD100K dataset show that the transferred night vehicle images are clear and meet the requirements. The precision, recall, mAP@.5, and mAP@.5:.95 reached 0.696, 0.292, 0.761, and 0.454, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.3904-3922
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• 2022

As a research hotspot, pedestrian detection has a wide range of applications in the field of computer vision in recent years. However, current pedestrian detection methods have problems such as insufficient detection accuracy and large models that are not suitable for large-scale deployment. In view of these problems mentioned above, a lightweight pedestrian detection and early warning method using a new model called you only look once (Yolov5) is proposed in this paper, which utilizing advantages of Yolov5s model to achieve accurate and fast pedestrian recognition. In addition, this paper also optimizes the loss function of the batch normalization (BN) layer. After sparsification, pruning and fine-tuning, got a lot of optimization, the size of the model on the edge of the computing power is lower equipment can be deployed. Finally, from the experimental data presented in this paper, under the training of the road pedestrian dataset that we collected and processed independently, the Yolov5s model has certain advantages in terms of precision and other indicators compared with traditional single shot multiBox detector (SSD) model and fast region-convolutional neural network (Fast R-CNN) model. After pruning and lightweight, the size of training model is greatly reduced without a significant reduction in accuracy, and the final precision reaches 87%, while the model size is reduced to 7,723 KB.

• Smart Structures and Systems
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• 제32권1호
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• pp.23-35
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• 2023

Steel bolted joint is an important part of steel structure, and its damage directly affects the bearing capacity and durability of steel structure. Currently, the existing research mainly focuses on the identification of corroded bolts and corroded bolts respectively, and there are few studies on multiple states. A detection framework of corroded and loosened bolts is proposed in this study, and the innovations can be summarized as follows: (i) Vision Transformer (ViT) is introduced to replace the third and fourth C3 module of you-only-look-once version 5s (YOLOv5s) algorithm, which increases the attention weights of feature channels and the feature extraction capability. (ii) Three states of the steel bolts are considered, including corroded bolt, bolt missing and clean bolt. (iii) Line segment detector (LSD) is introduced for bolt rotation angle calculation, which realizes bolt looseness detection. The improved YOLOv5s model was validated on the dataset, and the mean average precision (mAP) was increased from 0.902 to 0.952. In terms of a lab-scale joint, the performance of the LSD algorithm and the Hough transform was compared from different perspective angles. The error value of bolt loosening angle of the LSD algorithm is controlled within 1.09%, less than 8.91% of the Hough transform. Furthermore, the proposed framework was applied to fullscale joints of a steel bridge in China. Synthetic images of loosened bolts were successfully identified and the multiple states were well detected. Therefore, the proposed framework can be alternative of monitoring steel bolted joints for management department.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.121-126
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• 2022

The number of vehicles has increased exponentially over the past 20 years due to technological advancements. It is becoming almost impossible to manually control and manage the traffic in a city like Karachi. Without license plate recognition, traffic management is impossible. The Framework for License Plate Detection & Recognition to overcome these issues is proposed. License Plate Detection & Recognition is primarily performed in two steps. The first step is to accurately detect the license plate in the given image, and the second step is to successfully read and recognize each character of that license plate. Some of the most common algorithms used in the past are based on colour, texture, edge-detection and template matching. Nowadays, many researchers are proposing methods based on deep learning. This research proposes a framework for License Plate Detection & Recognition using a custom YOLOv5 Object Detector, image segmentation techniques, and Tesseract's optical character recognition OCR. The accuracy of this framework is 0.89.

• 한국ITS학회 논문지
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• 제21권3호
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• pp.83-95
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• 2022

본 논문은 자율주행 차량 적용을 위한 객체 검출과 거리 추정을 수행하는 시스템을 제안한다. 객체 검출은 최근 활발하게 사용되는 딥러닝 모델 YOLOv4의 특성을 이용해서 입력 이미지 비율에 맞춰 분할 grid를 조정하고 자체 데이터셋으로 전이학습된 네트워크로 수행한다. 검출된 객체까지의 거리는 bounding box와 homography를 이용해 추정한다. 실험 결과 제안하는 방법에서 전반적인 검출 성능 향상과 실시간에 가까운 처리 속도를 보였다. 기존 YOLOv4 대비 전체 mAP는 4.03% 증가했다. 도심로 주행시 빈출하는 보행자, 차량 및 공사장 고깔(cone), PE드럼(drum) 등의 객체 인식 정확도가 향상되었다. 처리 속도는 약 55 FPS이다. 거리 추정 오차는 X 좌표 평균 약 5.25m, Y 좌표 평균 0.97m으로 나타났다.

• 한국멀티미디어학회논문지
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• 제24권5호
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• pp.659-666
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• 2021

With the continuous development of deep learning, human behavior recognition algorithms have achieved good results. However, in a multi-person recognition environment, the complex behavior environment poses a great challenge to the efficiency of recognition. To this end, this paper proposes a multi-person pose estimation model. First of all, the human detectors in the top-down framework mostly use the two-stage target detection model, which runs slow down. The single-stage YOLOv3 target detection model is used to effectively improve the running speed and the generalization of the model. Depth separable convolution, which further improves the speed of target detection and improves the model's ability to extract target proposed regions; Secondly, based on the feature pyramid network combined with context semantic information in the pose estimation model, the OHEM algorithm is used to solve difficult key point detection problems, and the accuracy of multi-person pose estimation is improved; Finally, the Euclidean distance is used to calculate the spatial distance between key points, to determine the similarity of postures in the frame, and to eliminate redundant postures.