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Yimin Ma, Yi Xu, Yunqing Liu, Fei Yan, Qiong Zhang, Qi Li and Quanyang Liu
In recent years, deep convolutional neural networks with multi-scale features have been widely used in image super-resolution reconstruction (ISR), and the quality of the generated images has been significantly improved compared with traditional methods....
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Fangbin Wang, Zini Wang, Zhong Chen, Darong Zhu, Xue Gong and Wanlin Cong
To overcome the deficiencies in segmenting hot spots from thermal infrared images, such as difficulty extracting the edge features, low accuracy, and a high missed detection rate, an improved Mask R-CNN photovoltaic hot spot thermal image segmentation al...
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Hui Luo, Jiamin Li, Lianming Cai and Mingquan Wu
Automatic pavement crack detection is crucial for reducing road maintenance costs and ensuring transportation safety. Although convolutional neural networks (CNNs) have been widely used in automatic pavement crack detection, they cannot adequately model ...
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Xin Jin, Cheng Lin, Jiangtao Ji, Wenhao Li, Bo Zhang and Hongbin Suo
The extraction of navigation lines plays a crucial role in the autonomous navigation of agricultural robots. This work offers a method of ridge navigation route extraction, based on deep learning, to address the issues of poor real-time performance and l...
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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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Chunxian Wang, Xiaoxing Wang, Yiwen Wang, Shengchao Hu, Hongyang Chen, Xuehai Gu, Junchi Yan and Tao He
Neural architecture search (NAS) is a popular branch of automatic machine learning (AutoML), which aims to search for efficient network structures. Many prior works have explored a wide range of search algorithms for classification tasks, and have achiev...
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Soo-Jong Kim and Yong-Joo Chung
To alleviate the problem of performance degradation due to the varied sound durations of competing classes in sound event detection, we propose a method that utilizes multi-scale features for sound event detection. We employed a feature-pyramid component...
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Wenzhuo Zhang, Mingyang Yu, Xiaoxian Chen, Fangliang Zhou, Jie Ren, Haiqing Xu and Shuai Xu
Deep learning technology, such as fully convolutional networks (FCNs), have shown competitive performance in the automatic extraction of buildings from high-resolution aerial images (HRAIs). However, there are problems of over-segmentation and internal c...
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Shibiao Fang, Lin Mu, Kuan Liu and Darong Liu
In computer vision, pollutant detection is a highly concerning issue, and it has been widely used in the fields of pollutant identification, tracking, and precise positioning. In the ocean, oil tends to disperse into the water column as droplets under br...
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Ignazio Gallo, Riccardo La Grassa, Nicola Landro and Mirco Boschetti
In this paper, we provide an innovative contribution in the research domain dedicated to crop mapping by exploiting the of Sentinel-2 satellite images time series, with the specific aim to extract information on ?where and when? crops are grown. The fina...
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