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Yu Zhang, Jiajun Niu, Zezhong Huang, Chunlei Pan, Yueju Xue and Fengxiao Tan
An algorithm model based on computer vision is one of the critical technologies that are imperative for agriculture and forestry planting. In this paper, a vision algorithm model based on StyleGAN and improved YOLOv5s is proposed to detect sandalwood tre...
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Xingdong Sun, Yukai Zheng, Delin Wu and Yuhang Sui
The key technology of automated apple harvesting is detecting apples quickly and accurately. The traditional detection methods of apple detection are often slow and inaccurate in unstructured orchards. Therefore, this article proposes an improved YOLOv5s...
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Ying-Tung Hsiao, Jia-Shing Sheu, Hsu Ma
Pág. 42 - 49
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Joanna Kulawik, Mariusz Kubanek and Sebastian Garus
This research aimed to develop a system for classifying horizontal road signs as correct or with poor visibility. In Poland, road markings are applied by using a specialized white, reflective paint and require periodic repainting. Our developed system is...
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Ka Seng Chou, Teng Lai Wong, Kei Long Wong, Lu Shen, Davide Aguiari, Rita Tse, Su-Kit Tang and Giovanni Pau
This research addresses the challenges of visually impaired individuals? independent travel by avoiding obstacles. The study proposes a distance estimation method for uncontrolled three-dimensional environments to aid navigation towards labeled target ob...
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Rui Ren, Haixia Sun, Shujuan Zhang, Ning Wang, Xinyuan Lu, Jianping Jing, Mingming Xin and Tianyu Cui
To detect quickly and accurately ?Yuluxiang? pear fruits in non-structural environments, a lightweight YOLO-GEW detection model is proposed to address issues such as similar fruit color to leaves, fruit bagging, and complex environments. This model impro...
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Viet Q. Vu, Minh-Quang Tran, Mohammed Amer, Mahesh Khatiwada, Sherif S. M. Ghoneim and Mahmoud Elsisi
Facial mask detection technology has become increasingly important even beyond the context of the COVID-19 pandemic. Along with the advancement in facial recognition technology, face mask detection has become a crucial feature for various applications. T...
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Huishan Li, Lei Shi, Siwen Fang and Fei Yin
Aiming at the problem of accurately locating and identifying multi-scale and differently shaped apple leaf diseases from a complex background in natural scenes, this study proposed an apple leaf disease detection method based on an improved YOLOv5s model...
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Pavel Laptev, Sergey Litovkin, Sergey Davydenko, Anton Konev, Evgeny Kostyuchenko and Alexander Shelupanov
This paper compares neural networks, specifically Unet, MobileNetV2, VGG16 and YOLOv4-tiny, for image segmentation as part of a study aimed at finding an optimal solution for price tag data analysis. The neural networks considered were trained on an indi...
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Wendou Yan, Xiuying Wang and Shoubiao Tan
This paper proposes the You Only Look Once (YOLO) dependency fusing attention network (DFAN) detection algorithm, improved based on the lightweight network YOLOv4-tiny. It combines the advantages of fast speed of traditional lightweight networks and high...
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