• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.7-11
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• 2022

Object detection technology is one of the main research topics in the field of computer vision and has established itself as an essential base technology for implementing various vision systems. Recent DNN (Deep Neural Networks)-based algorithms achieve much higher recognition accuracy than traditional algorithms. However, it is well-known that the DNN model inference operation requires a relatively high computational power. In this paper, we analyze the inference time complexity of the state-of-the-art object detection architecture Yolov7 in various environments. Specifically, we compare and analyze the time complexity of four types of the Yolov7 model, YOLOv7-tiny, YOLOv7, YOLOv7-X, and YOLOv7-E6 when performing inference operations using CPU and GPU. Furthermore, we analyze the time complexity variation when inferring the same models using the Pytorch framework and the Onnxruntime engine.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.195-204
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• 2024

In this paper, we investigate the comparative performance of two leading object detection architectures, YOLOv7 and Detection Transformer (DETR), across varying levels of data augmentation using CycleGAN. Our experiments focus on chest scan images within the context of biomedical informatics, specifically targeting the detection of abnormalities. The study reveals that YOLOv7 consistently outperforms DETR across all levels of augmented data, maintaining better performance even with 75% augmented data. Additionally, YOLOv7 demonstrates significantly faster convergence, requiring approximately 30 epochs compared to DETR's 300 epochs. These findings underscore the superiority of YOLOv7 for object detection tasks, especially in scenarios with limited data and when rapid convergence is essential. Our results provide valuable insights for researchers and practitioners in the field of computer vision, highlighting the effectiveness of YOLOv7 and the importance of data augmentation in improving model performance and efficiency.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.297-309
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• 2023

The degradation of coastal ecosystems and fishery environments is accelerating due to the recent phenomenon of invertebrate grazers. To effectively monitor and implement preventive measures for this phenomenon, the adoption of remote sensing-based monitoring technology for extensive maritime areas is imperative. In this study, we compared and analyzed the robustness of deep learning-based object detection modelsfor detecting and monitoring invertebrate grazersfrom underwater videos. We constructed an image dataset targeting seven representative species of invertebrate grazers in the coastal waters of South Korea and trained deep learning-based object detection models, You Only Look Once (YOLO)v7 and YOLOv8, using this dataset. We evaluated the detection performance and speed of a total of six YOLO models (YOLOv7, YOLOv7x, YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x) and conducted robustness evaluations considering various image distortions that may occur during underwater filming. The evaluation results showed that the YOLOv8 models demonstrated higher detection speed (approximately 71 to 141 FPS [frame per second]) compared to the number of parameters. In terms of detection performance, the YOLOv8 models (mean average precision [mAP] 0.848 to 0.882) exhibited better performance than the YOLOv7 models (mAP 0.847 to 0.850). Regarding model robustness, it was observed that the YOLOv7 models were more robust to shape distortions, while the YOLOv8 models were relatively more robust to color distortions. Therefore, considering that shape distortions occur less frequently in underwater video recordings while color distortions are more frequent in coastal areas, it can be concluded that utilizing YOLOv8 models is a valid choice for invertebrate grazer detection and monitoring in coastal waters.

• Annual Conference of KIPS
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• 2023.11a
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• pp.1161-1162
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• 2023

모바일 로봇의 자율주행을 위하여 인터넷이 제약된 환경에서도 가능한 Edge computing 에서의 Object Detection 이 필수적이다. 본 논문에서는 이를 위해 Orin 보드에서 YOLOv7 과 Complex_YOLOv4 를 구현하였다. 직접 취득한 데이터를 통해 YOLOv7 을 구현한 결과 0.56 의 mAP 로 프레임당 133ms 가 소요되었다. Kitti Dataset 을 통해 Complex_YOLOv4 를 구현한 결과 0.88 의 mAP 로 프레임당 236ms 가 소요되었다. Comple_YOLOv4 가 YOLOv7 보다 더 많은 데이터를 예측하기에 시간은 더 소요되지만 높은 정확성을 가지는 것을 확인할 수 있었다.

• Annual Conference of KIPS
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• 2023.11a
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• pp.154-155
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• 2023

오픈 소스인 YOLO(You Only Look Once) 객체 탐지 알고리즘이 공개된 이후, 산업 현장에서는 고성능 컴퓨터에서 벗어나 효율과 특수한 환경에 사용하기 위해 임베디드 시스템에 도입하고 있다. 그러나, NVIDIA의 Jetson nano의 경우, Pytorch의 YOLOv7 딥러닝 모델에 대한 추론이 진행되지 않는다. 따라서 제한적인 전력과 메모리, 연산능력 최적화 과정은 필수적이다. 본 논문은 NVIDIA의 임베디드 플랫폼 Jetson 계열의 Xavier NX, Orin AGX, Nano에서 딥러닝 모델을 적용하기 위한 최적화 과정과 플랫폼에서 다양한 크기의 YOLOv7의 PyTorch 모델들을 Tensor RT로 변환하여 FPS(Frames Per Second)를 측정 및 비교한다. 측정 결과를 통해, 각 임베디드 플랫폼에서 YOLOv7 모델의 추론은 Tensor RT는 Pytorch에서 약 4.1배 적은 FPS 변동성과 약 2.25배 정도의 FPS 속도향상을 보였다.

• Annual Conference of KIPS
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• 2023.11a
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• pp.1215-1217
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• 2023

가정에서 대형 폐기물을 배출하고 수거하는 과정에서 폐기물을 수동적으로 분류를 하는 것은 시간이 많이 소요되는 작업이다. 본 논문에서는 YOLOv4, 5, 7 모델을 비교하여 실생활에 사용가능한 대형 폐기물 탐지에 가장 적합한 모델을 찾는다. 이미지 증강 전 결과는 YOLOv7이 가장 좋은 성능을 보였다. 배출자가 촬영하는 각도나 위치, 시간 등의 변수를 고려하고자 증강을 시도하였고 증강 후 탐지 결과도 YOLOv7이 F1-score 93 %, mAP 96.6% 로 다른 모델보다 전체적으로 더 좋은 성능을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2199-2213
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• 2024

To enhance the quality of defect detection for Printed Circuit Board Assembly (PCBA) during electronic product manufacturing, this study primarily focuses on optimizing the YOLOv7-based method for PCBA defect detection. In this method, the Mish, a smoother function, replaces the Leaky ReLU activation function of YOLOv7, effectively expanding the network's information processing capabilities. Concurrently, a Squeeze-and-Excitation attention mechanism (SEAM) has been integrated into the head of the model, significantly augmenting the precision of small target defect detection. Additionally, considering angular loss, compared to the CIoU loss function in YOLOv7, the SIoU loss function in the paper enhances robustness and training speed and optimizes inference accuracy. In terms of data preprocessing, this study has devised a brightness adjustment data enhancement technique based on split-filtering to enrich the dataset while minimizing the impact of noise and lighting on images. The experimental results under identical training conditions demonstrate that our model exhibits a 9.9% increase in mAP value and an FPS increase to 164 compared to the YOLOv7. These indicate that the method proposed has a superior performance in PCBA defect detection and has a specific application value.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.193-205
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• 2023

A large amount of floating debris from land-based sources during heavy rainfall has negative social, economic, and environmental impacts, but there is a lack of monitoring systems for floating debris accumulation areas and amounts. With the recent development of artificial intelligence technology, there is a need to quickly and efficiently study large areas of water systems using drone imagery and deep learning-based object detection models. In this study, we acquired various images as well as drone images and trained with You Only Look Once (YOLO)v5s and the recently developed YOLO7 and YOLOv8s to compare the performance of each model to propose an efficient detection technique for land-based floating debris. The qualitative performance evaluation of each model showed that all three models are good at detecting floating debris under normal circumstances, but the YOLOv8s model missed or duplicated objects when the image was overexposed or the water surface was highly reflective of sunlight. The quantitative performance evaluation showed that YOLOv7 had the best performance with a mean Average Precision (intersection over union, IoU 0.5) of 0.940, which was better than YOLOv5s (0.922) and YOLOv8s (0.922). As a result of generating distortion in the color and high-frequency components to compare the performance of models according to data quality, the performance degradation of the YOLOv8s model was the most obvious, and the YOLOv7 model showed the lowest performance degradation. This study confirms that the YOLOv7 model is more robust than the YOLOv5s and YOLOv8s models in detecting land-based floating debris. The deep learning-based floating debris detection technique proposed in this study can identify the spatial distribution of floating debris by category, which can contribute to the planning of future cleanup work.

• Journal of Internet of Things and Convergence
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• v.10 no.5
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• pp.185-196
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• 2024

Increasing urbanization and road development have led to severe wildlife roadkill incidents, posing a significant social challenge. Especially in Korea, the high road density increases the exposure of wildlife to roadkill risks. This study proposes a wildlife object detection system using the latest object detection technology, YOLOv8, to mitigate domestic wildlife roadkill incidents. Using Roboflow for preprocessing, six mammal species were selected and the YOLOv8 model was trained, achieving high accuracy with mAP50 of 0.986 and mAP50-95 of 0.86. Comparative experiments with YOLOv5 and YOLOv7 demonstrated the superior performance of YOLOv8. The proposed system effectively detects animals even with protective coloration, contributing to the reduction of roadkill incidents and assisting in the design and placement of ecological corridors and guide fences. This research provides an important technological foundation for improving wildlife safety and maintaining ecological balance in urbanized areas.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.157-165
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• 2020

Aiming at the development of neural network and self-driving data set, it is also an idea to improve the performance of network model to detect moving objects by dividing the data set. In Darknet network framework, the YOLOv4 (You Only Look Once v4) network model was used to train and test Udacity data set. According to 7 proportions of the Udacity data set, it was divided into three subsets including training set, validation set and test set. K-means++ algorithm was used to conduct dimensional clustering of object boxes in 7 groups. By adjusting the super parameters of YOLOv4 network for training, Optimal model parameters for 7 groups were obtained respectively. These model parameters were used to detect and compare 7 test sets respectively. The experimental results showed that YOLOv4 can effectively detect the large, medium and small moving objects represented by Truck, Car and Pedestrian in the Udacity data set. When the ratio of training set, validation set and test set is 7:1.5:1.5, the optimal model parameters of the YOLOv4 have highest detection performance. The values show mAP50 reaching 80.89%, mAP75 reaching 47.08%, and the detection speed reaching 10.56 FPS.