A5103095787- Industrial Vision Systems and Defect Detection
- Advanced Neural Network Applications
- Engineering Diagnostics and Reliability
- Remote Sensing and LiDAR Applications
- Image Enhancement Techniques
- Wood and Agarwood Research
- Fault Detection and Control Systems
- Machine Fault Diagnosis Techniques
- Robotics and Sensor-Based Localization
- Advanced Image and Video Retrieval Techniques
- Video Surveillance and Tracking Methods
- Advanced Image Fusion Techniques
Hainan University
2024
Zhuhai Institute of Advanced Technology
2024
Anhui University
2023-2024
<title>Abstract</title> Unmanned aerial vehicles (UAVs), as an emerging technology with vast application prospects, produce distinctive images due to factors, such their shooting environment, altitude, and equipment. These UAV-captured possess unique characteristics compared traditional pictures, including high resolution, abundant presence of small objects, object density. However, most existing detection networks primarily focus on detecting large resulting in limitations terms targets....
Due to the detrimental impact of fog on image quality, dehazing maritime images is essential for applications such as safe navigation, surveillance, environmental monitoring, and marine research. Traditional techniques, which are dependent presupposed conditions, often fail perform effectively, particularly when processing sky regions within in these conditions not met. This study proposes an adaptive area segmentation dark channel prior method. effectively addresses challenges associated...
Wood is an extremely important foundational building material. Rapid and accurate wood species recognition crucial for the rational application of preventing economic losses caused by misidentification species. Current research primarily focuses on image preprocessing use Convolutional Neural Networks (CNNs) recognition. Most existing methods employ non-lightweight CNN models, making it difficult models to achieve rapid Addressing above issues, this paper introduces a hybrid model that...