161   Artículos

 
en línea
Xuejun Yue, Haifeng Li, Qingkui Song, Fanguo Zeng, Jianyu Zheng, Ziyu Ding, Gaobi Kang, Yulin Cai, Yongda Lin, Xiaowan Xu and Chaoran Yu    
Existing disease detection models for deep learning-based monitoring and prevention of pepper diseases face challenges in accurately identifying and preventing diseases due to inter-crop occlusion and various complex backgrounds. To address this issue, w... ver más
Revista: Agronomy    Formato: Electrónico

 
en línea
Zongshun Wang, Ce Li, Jialin Ma, Zhiqiang Feng and Limei Xiao    
In this study, we introduce a novel framework for the semantic segmentation of point clouds in autonomous driving scenarios, termed PVI-Net. This framework uniquely integrates three different data perspectives?point clouds, voxels, and distance maps?exec... ver más
Revista: Information    Formato: Electrónico

 
en línea
Zhendong He, Wenbin Yang, Yanjie Liu, Anping Zheng, Jie Liu, Taishan Lou and Jie Zhang    
Ensuring the safety of transmission lines necessitates effective insulator defect detection. Traditional methods often need more efficiency and accuracy, particularly for tiny defects. This paper proposes an innovative insulator defect recognition method... ver más
Revista: Information    Formato: Electrónico

 
en línea
Zhe Yin, Mingkang Peng, Zhaodong Guo, Yue Zhao, Yaoyu Li, Wuping Zhang, Fuzhong Li and Xiaohong Guo    
With the advancement of machine vision technology, pig face recognition has garnered significant attention as a key component in the establishment of precision breeding models. In order to explore non-contact individual pig recognition, this study propos... ver más
Revista: Agriculture    Formato: Electrónico

 
en línea
Wei Zhuang, Zhiheng Li, Ying Wang, Qingyu Xi and Min Xia    
Predicting photovoltaic (PV) power generation is a crucial task in the field of clean energy. Achieving high-accuracy PV power prediction requires addressing two challenges in current deep learning methods: (1) In photovoltaic power generation prediction... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Yiming Mo, Lei Wang, Wenqing Hong, Congzhen Chu, Peigen Li and Haiting Xia    
The intrusion of foreign objects on airport runways during aircraft takeoff and landing poses a significant safety threat to air transportation. Small-scale Foreign Object Debris (FOD) cannot be ruled out on time by traditional manual inspection, and the... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Qiyan Li, Zhi Weng, Zhiqiang Zheng and Lixin Wang    
The decrease in lake area has garnered significant attention within the global ecological community, prompting extensive research in remote sensing and computer vision to accurately segment lake areas from satellite images. However, existing image segmen... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Baobao Liu, Heying Wang, Zifan Cao, Yu Wang, Lu Tao, Jingjing Yang and Kaibing Zhang    
Defect detection holds significant importance in improving the overall quality of fabric manufacturing. To improve the effectiveness and accuracy of fabric defect detection, we propose the PRC-Light YOLO model for fabric defect detection and establish a ... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Can Li, Hua Sun, Changhong Wang, Sheng Chen, Xi Liu, Yi Zhang, Na Ren and Deyu Tong    
In order to safeguard image copyrights, zero-watermarking technology extracts robust features and generates watermarks without altering the original image. Traditional zero-watermarking methods rely on handcrafted feature descriptors to enhance their per... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Lianlian He, Hao Li and Rui Zhang    
Recent advances in knowledge graphs show great promise to link various data together to provide a semantic network. Place is an important part in the big picture of the knowledge graph since it serves as a powerful glue to link any data to its georeferen... ver más
Revista: ISPRS International Journal of Geo-Information    Formato: Electrónico

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