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Hechen Yang, Xin Zhao, Tao Jiang, Jinghua Zhang, Peng Zhao, Ao Chen, Marcin Grzegorzek, Shouliang Qi, Yueyang Teng and Chen Li
Currently, the field of transparent image analysis has gradually become a hot topic. However, traditional analysis methods are accompanied by large amounts of carbon emissions, and consumes significant manpower and material resources. The continuous deve...
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Xinzhi Wang, Mengyue Li, Quanyi Liu, Yudong Chang and Hui Zhang
The accurate analysis of multi-scale flame development plays a crucial role in improving firefighting decisions and facilitating smart city establishment. However, flames? non-rigid nature and blurred edges present challenges in achieving accurate segmen...
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Yefeng Sun, Liang Gong, Wei Zhang, Bishu Gao, Yanming Li and Chengliang Liu
Drivable area detection is crucial for the autonomous navigation of agricultural robots. However, semi-structured agricultural roads are generally not marked with lanes and their boundaries are ambiguous, which impedes the accurate segmentation of drivab...
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Esraa Elhariri, Nashwa El-Bendary and Shereen A. Taie
Crack detection on historical surfaces is of significant importance for credible and reliable inspection in heritage structural health monitoring. Thus, several object detection deep learning models are utilized for crack detection. However, the majority...
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Wei Zhao, Yi Fu, Xiaosong Wei and Hai Wang
This paper proposed an improved image semantic segmentation method based on superpixels and conditional random fields (CRFs). The proposed method can take full advantage of the superpixel edge information and the constraint relationship among different p...
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Jingxiong Lei, Xuzhi Liu, Haolang Yang, Zeyu Zeng and Jun Feng
High-resolution remote sensing images (HRRSI) have important theoretical and practical value in urban planning. However, current segmentation methods often struggle with issues like blurred edges and loss of detailed information due to the intricate back...
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Yongjiang Mao, Wenjuan Ren, Xipeng Li, Zhanpeng Yang and Wei Cao
With the progress of signal processing technology and the emergence of new system radars, the space electromagnetic environment becomes more and more complex, which puts forward higher requirements for the deinterleaving method of radar signals. Traditio...
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Tao Wang, Jingjing Chen, Li Liu and Lingling Guo
Recently, the deep learning technology has been adopted in the study of traditional village landscape. More precisely, it?s usually used to explore the representation of cultural heritage and the diversity of heritage information. In this study, we compr...
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Yu Guo, Guigen Nie, Wenliang Gao and Mi Liao
Semantic segmentation is a critical task in computer vision that aims to assign each pixel in an image a corresponding label on the basis of its semantic content. This task is commonly referred to as dense labeling because it requires pixel-level classif...
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Zahra Ameli, Shabnam Jafarpoor Nesheli and Eric N. Landis
The application of deep learning (DL) algorithms has become of great interest in recent years due to their superior performance in structural damage identification, including the detection of corrosion. There has been growing interest in the application ...
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Calimanut-Ionut Cira, Martin Kada, Miguel-Ángel Manso-Callejo, Ramón Alcarria and Borja Bordel Sanchez
The road surface area extraction task is generally carried out via semantic segmentation over remotely-sensed imagery. However, this supervised learning task is often costly as it requires remote sensing images labelled at the pixel level, and the result...
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Carlos Pena-Caballero, Dongchul Kim, Adolfo Gonzalez, Osvaldo Castellanos, Angel Cantu and Jungseok Ho
Infrastructure is a significant factor in economic growth for systems of government. In order to increase economic productivity, maintaining infrastructure quality is essential. One of the elements of infrastructure is roads. Roads are means which help l...
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Quanchun Jiang, Olamide Timothy Tawose, Songwen Pei, Xiaodong Chen, Linhua Jiang, Jiayao Wang and Dongfang Zhao
In this paper, we propose a semantic segmentation method based on superpixel region merging and convolutional neural network (CNN), referred to as regional merging neural network (RMNN). Image annotation has always been an important role in weakly-superv...
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In this paper, we propose a semantic segmentation method based on superpixel region merging and convolutional neural network (CNN), referred to as regional merging neural network (RMNN). Image annotation has always been an important role in weakly-superv...
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Jianming Zhang, Chaoquan Lu, Jin Wang, Lei Wang and Xiao-Guang Yue
In civil engineering, the stability of concrete is of great significance to safety of people?s life and property, so it is necessary to detect concrete damage effectively. In this paper, we treat crack detection on concrete surface as a semantic segmenta...
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Hang Li, Shengjie Zhao and Hao Deng
The extraction of community-scale green infrastructure (CSGI) poses challenges due to limited training data and the diverse scales of the targets. In this paper, we reannotate a training dataset of CSGI and propose a three-stage transfer learning method ...
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Yunlong Dong, Wei Guo, Fusheng Zha, Yizhou Liu, Chen Chen and Lining Sun
The friction and stiffness properties of the terrain are very important pieces of information for mobile robots in motion control, dynamics parameter adjustment, trajectory planning, etc. Inferring the friction and stiffness properties in advance can imp...
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Yuhuan Wu and Yonghong Wu
Salient object detection (SOD) aims to identify the most visually striking objects in a scene, simulating the function of the biological visual attention system. The attention mechanism in deep learning is commonly used as an enhancement strategy which e...
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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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César Jesús-Valls, Marc Granado-González, Thorsten Lux, Tony Price and Federico Sánchez
Recently, we proposed a novel range detector concept named ASTRA. ASTRA is optimized to accurately measure (better than 1%) the residual energy of protons with kinetic energies in the range from tens to a few hundred MeVs at a very high rate of O(" role=...
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