Single Sensor and Multi-Sensor Object Identification and Detection with Deep Learning

Dear Colleagues,

Deep learning has become popular in object detection and recognition. While many works have been dedicated to computer vision based on camera, there have been improvements in developing deep learning-based detection and identification for other sensors such as radar, infrared (IR), lidar, hyperspectral and RGB-D sensing. Besides single sensor or multi-sensor object detection, multi-sensor identification and multi-modal deep learning are of great interest to enhance the detection and recognition performance. This Special Issue calls for original and novel object detection and recognition methods based on deep learning for single sensor and multiple sensors. The topics of interest include, but are not limited to:

• Image object detection and identification;
• Object detection and classification in camera networks;
• 3D computer vision;
• RGB-D object detection and recognition;
• Radar target detection and identification;
• Lidar object detection and identification;
• IR object detection and recognition;
• Object detection and classification in hyperspectral data;
• Multispectral object detection and recognition;
• Multi-sensor object detection and classification;
• Multi-modal deep learning;
• Object detection and identification in IoT;
• Object detection and classification in unmanned aerial vehicle(UAV) imagery;
• Object detection and classification for autonomous driving. 

Prof. Dr. Henry Leung
Dr. Kevin I-Kai Wang
Dr. Wasim Ahmad
Dr. Peng Wang
Dr. Hao Zhu
Guest Editors

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