65   Artículos

 
en línea
Zhikai Jiang, Li Su and Yuxin Sun    
Accurate ship object detection ensures navigation safety and effective maritime traffic management. Existing ship target detection models often have the problem of missed detection in complex marine environments, and it is hard to achieve high accuracy a... ver más
Revista: Journal of Marine Science and Engineering    Formato: Electrónico

 
en línea
Juanli Jing, Menglin Zhai, Shiqing Dou, Lin Wang, Binghai Lou, Jichi Yan and Shixin Yuan    
The accurate identification of citrus fruits is important for fruit yield estimation in complex citrus orchards. In this study, the YOLOv7-tiny-BVP network is constructed based on the YOLOv7-tiny network, with citrus fruits as the research object. This n... ver más
Revista: Agriculture    Formato: Electrónico

 
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
Xinmin Li, Yingkun Wei, Jiahui Li, Wenwen Duan, Xiaoqiang Zhang and Yi Huang    
Object detection in unmanned aerial vehicle (UAV) images has become a popular research topic in recent years. However, UAV images are captured from high altitudes with a large proportion of small objects and dense object regions, posing a significant cha... 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
Norah Fahd Alhussainan, Belgacem Ben Youssef and Mohamed Maher Ben Ismail    
Brain tumor diagnosis traditionally relies on the manual examination of magnetic resonance images (MRIs), a process that is prone to human error and is also time consuming. Recent advancements leverage machine learning models to categorize tumors, such a... ver más
Revista: Computation    Formato: Electrónico

 
en línea
Linhua Zhang, Ning Xiong, Wuyang Gao and Peng Wu    
With the exponential growth of remote sensing images in recent years, there has been a significant increase in demand for micro-target detection. Recently, effective detection methods for small targets have emerged; however, for micro-targets (even fewer... ver más
Revista: Information    Formato: Electrónico

 
en línea
Zhengyang Zhong, Lijun Yun, Feiyan Cheng, Zaiqing Chen and Chunjie Zhang    
This paper proposes a lightweight and efficient mango detection model named Light-YOLO based on the Darknet53 structure, aiming to rapidly and accurately detect mango fruits in natural environments, effectively mitigating instances of false or missed det... ver más
Revista: Agriculture    Formato: Electrónico

 
en línea
Ping Dong, Kuo Li, Ming Wang, Feitao Li, Wei Guo and Haiping Si    
In addition to the conventional situation of detecting a single disease on a single leaf in corn leaves, there is a complex phenomenon of multiple diseases overlapping on a single leaf (compound diseases). Current research on corn leaf disease detection ... ver más
Revista: Agriculture    Formato: Electrónico

 
en línea
Ayman Noor, Ziad Algrafi, Basil Alharbi, Talal H. Noor, Abdullah Alsaeedi, Reyadh Alluhaibi and Majed Alwateer    
Ambulance vehicles face a challenging issue in minimizing the response time for an emergency call due to the high volume of traffic and traffic signal delays. Several research works have proposed ambulance vehicle detection approaches and techniques to p... ver más
Revista: Applied Sciences    Formato: Electrónico

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