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Ship detection in SAR Image using YOLOv2
2018 37th Chinese Control Conference (CCC), 2018Ship detection in SAR images is a challenge and has traditionally been carried out using pixel based algorithms such as CFAR, in this paper we use a deep learning based algorithm called YOLOv2 for the aforementioned task and test its performance on three datasets, at different resolution and quality, with two datasets called DS1 and DS2 consisting of ...
Hamza Mehdi Khan, Cai Yunze
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Fast Classification and Detection of Fish Images with YOLOv2
2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO), 2018In this paper, we introduce a convolutional neural network based on the state-of-the-art detector, named YOLOv2, to classify and detect images of fish. Meanwhile, we have adopted the relevant customization techniques to optimize the architecture of model.
Mengfan Wang +5 more
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Environment Recognition for Electric Wheelchair Based on YOLOv2
Proceedings of the 3rd International Conference on Biomedical Signal and Image Processing, 2018At present, the aging population is growing in Japan. Along with that, the need for the utilization of welfare equipment is increasing. Electric wheelchair, a convenient transportation tool, is popularized rapidly. However, many accidents have occurred by using electric wheelchair, and the dangers for driving are pointed out.
Yuki Sakai +3 more
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Performance evaluation of YOLOv2 and modified YOLOv2 using face mask detection
Multimedia Tools and Applications, 2023SriPadma Parupalli +4 more
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A Real-Time Vehicle Logo Detection Method Based on Improved YOLOv2
2020Confirming the vehicle information in the surveillance video is an important issue in the intelligent transportation system at present. As a key and fixed feature, the vehicle logo can play a role in assisting the discrimination. According to the characteristics of the vehicle logo image, we propose a high-efficiency logo detection method based on the ...
Kangning Yin +4 more
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Pedestrain Detection in Infrared Images with Improved YOLOv2 Network
2020 IEEE International Conference on Information Technology,Big Data and Artificial Intelligence (ICIBA), 2020With the progress of unmanned driving technology and the attention of security problems, pedestrian detection is getting more and more researchers' participation. Since 2012, the application of deep convolutional network has greatly improved the accuracy and detection speed of pedestrian detection.
Chenshi Du, Peng Song, Xin Ma
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A real-time multi-class multi-object tracker using YOLOv2
2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), 2017Multi-class multi-object tracking is an important problem for real-world applications like surveillance system, gesture recognition, and robot vision system. However, building a multi-class multi-object tracker that works in real-time is difficult due to low processing speed for detection, classification, and data association tasks.
KangUn Jo +3 more
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Vehicle Logo Detection Based on Modified YOLOv2
2019Vehicle logo detection technology is one of the research directions in the application of intelligent transportation systems. It is an important extension of detection technology based on license plates and motorcycle types. A vehicle logo is characterized by uniqueness, conspicuousness, and diversity.
Shuo Yang +3 more
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Traffic Sign Recognition and Classification Using YOLOv2, Faster RCNN and SSD
2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2019In the walk of Advanced Driving Assistance Systems (ADAS), Intelligent Driving and Traffic safety, Object detection plays a crucial role in the upcoming genesis of self-governing vehicles. Traditional computer vision and machine learning advances for object detection confront challenges against the difficult image backgrounds and environment conditions
Priya Garg +2 more
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Optimization algorithm of manhole recognition based on YOLOv2
2022 14th International Conference on Machine Learning and Computing (ICMLC), 2022Mengzi Yin +3 more
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