Special Issue on Deep Learning for Precise and Efficient Object Detection
摘要截稿:
全文截稿: 2020-12-31
影响因子: 3.255
期刊难度:
CCF分类: C类
中科院JCR分区:
• 大类 : 计算机科学 - 3区
• 小类 : 计算机:人工智能 - 3区
Overview
Object detection is one of the most challenging and important tasks of computer vision and is widely used in applications such as autonomous vehicle, biometrics, video surveillance, and human-machine interactions. In the past five years, significant success has been achieved with the development of deep learning, especially deep convolutional neural networks. Typical categories of advanced object detection methods are one-stage, two-stage, and anchor-free methods. Nevertheless, the performance in accuracy and efficiency is far from satisfying. On the one hand, the average precision of state-of-the-art object detection methods is very low (e.g., merely about 40% on the COCO dataset). The performance is even worse for small and occluded objects. On the another hand, to obtain precision the detection speed is very low. It is challenging to get a satisfying trade-off between the detection precision and speed. Therefore, much efforts have to be engaged to remarkably improve the performance of object detection in both precision and efficiency.
This special issue will publish papers presenting state-of-the-art methods in dealing with the challenging problems of object detection within the framework of deep learning. We invite authors to submit manuscripts that are highly related to the topics of this special issue and which have not been published before. The topics of interest include, but are not limited to:
Anchor and Anchor-free object detection
Detecting small or occluded objects
Context and attention mechanism for object detection
Fast object detection algorithms
New backbone for object detection
Architecture search for object detection
3D object detection
Object detection in challenging conditions
Handling scale problems in object detection
Improving localization accuracy
Fusion of point cloud and images for object detection
Relationship between object detection and other computer vision tasks.