Special Issue "Deep Learning Based Object Detection"
Deadline for manuscript submissions: 31 August 2020.
Interests: low-level image processing; deep learning based object detection/classification multi-task learning; network compression; medical image processing
Object detection is one of the most important and challenging categories of computer vision and machine learning, which have been extensively utilized in various applications, such as video surveillance, autonomous vehicle, human–machine interaction, medical image analysis, and so on. Recently, significant improvement has been achieved as a result of the rapid development of deep learning, especially convolutional neural networks (CNNs).
To evaluate deep learning-based object detection methods, various databases have been introduced, and many researchers have endeavored to improve the performance of their proposed methodologies for the target database. There are mainstream benchmarks based on general object detection datasets, such as ImageNet, KITTI, and MS COCO. Even though significant improvements were achieved from previous shallow network-based methods for well-known datasets, unseen data from different environments or different applications suffered from relatively low performance.
This Special Issue will cover the most recent technical advances in all deep learning-based object recognition aspects, including theoretical issues on deep learning, real-world applications, practical object detection systems, and originally designed databases. Both transfer learning or semi-supervised learning of deep learning are welcome. Reviews and surveys of the state-of-the-art in deep learning-based object detection are also welcome. Topics of interest for this Special Issue include, but are not limited to, the following topics:
- Image/video-based object detection using deep learning
- Sensor fusion for object detection using deep learning
- Transfer learning for object detection
- Online learning for object detection
- Active learning for object detection
- Semi-supervised learning for object detection
- Deep learning-based object detection for real-world applications
- Object detection systems
- New database for object detection
- Survey for deep learning-based object detection
Dr. Youngbae Hwang
Manuscript Submission Information
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