Affiliations: [a] Department of Computer Science, ILMA University, Karachi, Pakistan | [b] School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China | [c] School of Artificial Intelligence, Xidian University, Xi’an, Shaanxi 710071, China | [d] School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
Correspondence:
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Corresponding authors: Shahid Karim and Shoulin Yin, Department of Computer Science, ILMA University, Karachi, Pakistan. E-mail: [email protected];[email protected].
Abstract: Traditional object detection algorithms and strategies are difficult to meet the requirements of data processing efficiency, performance, speed and intelligence in object detection. Through the study and imitation of the cognitive ability of the brain, deep learning can analyze and process the data features. It has a strong ability of visualization and becomes the mainstream algorithm of current object detection applications. Firstly, we have discussed the developments of traditional object detection methods. Secondly, the frameworks of object detection (e.g. Region-based CNN (R-CNN), Spatial Pyramid Pooling Network (SPP-NET), Fast-RCNN and Faster-RCNN) which combine region proposals and convolutional neural networks (CNNs) are briefly characterized for optical remote sensing applications. You only look once (YOLO) algorithm is the representative of the object detection frameworks (e.g. YOLO and Single Shot MultiBox Detector (SSD)) which transforms the object detection into a regression problem. The limitations of remote sensing images and object detectors have been highlighted and discussed. The feasibility and limitations of these approaches will lead researchers to prudently select appropriate image enhancements. Finally, the problems of object detection algorithms in deep learning are summarized and the future recommendations are also conferred.
Keywords: Object detection, optical remote sensing, neural networks, region proposal, deep learning
