State of the Art in Object Detection Based on Computer Vision and Image Processing

Dear Colleagues,

Object detection is one of the most fundamental and challenging topics in the field of remote sensing image analysis, where satellite, aerial images and UAVs can all be used for surveillance, tracking and positioning services. At the same time, many new artificial intelligence-based methods of detection are also being rapidly developed. Artificial intelligence, especially the fields of computer vision and deep neural networks, can be an extremely effective tool for automatic object detection, able to efficiently analyze large amounts of data. The purpose of object detection is to accurately locate and classify objects. However, unlike the excellent performance shown by deep learning in image classification, significant performance improvements are still required in object detection. Due to the complex attributes and variation of objects, most existing methods still have certain drawbacks, such as easily losing or mislocating objects, issues of how to use image processing to detect unknown classes, how to accurately detect 3D objects, etc.

It is our pleasure to announce the launch of a new Special Issue of Remote Sensing, the goal of which is to gather the latest research on the applications of remote sensing techniques including any or all aspects of image processing techniques for image enhancement, object detection and anomaly detection. At the same time, we also welcome papers in which artificial intelligence methods are not directly used in image processing but comprehensively used in multi-target detection. Articles may address, but are not limited to, the following topics.

• Object detection with sensor fusion;
• Small object detection;
• Object detection with multimodal information fusion;
• Occluded object detection;
• Weakly supervised/unsupervised object detection;
• Review papers and dataset for object detection.

Prof. Dr. Jong-Eun Ha
Prof. Dr. Hyoseok Hwang
Dr. Ronghui Zhan
Guest Editors

Manuscript Submission Information

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