Investigations of Object Detection in Images/Videos Using Various Deep Learning Techniques
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 11250
Special Issue Editors
Interests: image processing; pattern recognition; artificial intelligence; object detection
Special Issues, Collections and Topics in MDPI journals
Interests: image processing; pattern recognition; deep learning; object detection
Special Issue Information
Dear Colleagues,
Object detection is a mainstream and challenging branch of computer vision, and it has attracted much research attention in recent years because of its close relationship with video analysis and image understanding. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. The performance easily stagnates by constructing complex ensembles that combine multiple low-level image features with high-level contexts from object detectors and scene classifiers. With the rapid development in deep learning, more promising methods are proposed to address object detection. The deep learning-based models are able to learn semantic, high-level, and deeper features. However, the network architecture, training strategy and optimization function are still worth investigating. The deep learning-based models are data-driven and rely on the training dataset. Hence, it is essential to build the object detection datasets and investigate the few-shot learning methods. Furthermore, to achieve real-time object detection, it is necessary to improve the model efficiency and design light-weighted models. This Special Issue is aimed at addressing the following issues:
- Salient object detection, face detection and pedestrian detection.
- Object detection architecture designing.
- SAR object detection.
- Infrared object detection.
- Real-time object detection.
- Object detection datasets building.
- Few-shot object detection.
- Small object detection.
- Underwater object detection.
- Image enhancement and image generation.
- Data augmentation.
Dr. Junchao Zhang
Dr. Moran Ju
Dr. Xiangyue Zhang
Guest Editors
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Keywords
- object detection
- deep learning
- dataset building
- data augmentation
- image enhancement
- real-time object detection