Object Detection with Deep Learning

Dear Colleagues,

Object detection is one of the most important fundamental branches within the realm of computer vision. It has been widely applied in numerous real-world applications, such as security surveillance, autonomous driving, remote sensing scene analysis, robotic vision, and so on. The accurate localization and classification of objects within complex and diverse scenes represent critical challenges in object detection, limiting the development of object detection technology. With the rapid development of deep learning networks for object detection tasks, the performance of object detectors has achieved remarkable performance improvements and opened up new avenues for research. Deep-learning-based approaches have demonstrated significant success in addressing this challenge; consequently, convolutional neural networks (CNNs) have allowed us to understand rich representations of objects from raw image data. This Special Issue aims to furnish a thorough exploration of recent advancements and emerging trends in the domain of deep learning applied to object detection, including the development of novel architecture design, attention mechanisms for feature extraction, training methodologies, model compression, and various specific applications.

Topic areas include, but are not limited to, the following:

• Few-shot/zero-shot object detection;
• Weak/semi/unsupervised object detection;
• Model compression for object detectors;
• Small object detection;
• Rotated object detection

Dr. Shun Zhang
Dr. Feng Gao
Dr. Mingyang Ma
Guest Editors

Manuscript Submission Information

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• object detection
• deep learning
• few-shot object detection
• model compression
• rotated object detection