Mr. Ma Qi Profile

Ma Qi

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Proceedings Article | 20 December 2019 Paper

Fast detection and recognition method of UAV in sky background

Qi Ma, Bin Zhu, Zhengdong Cheng, Yang Zhang

Proceedings Volume 11209, 1120907 (2019) https://doi.org/10.1117/12.2542273

KEYWORDS: Unmanned aerial vehicles, Infrared imaging, Image registration, Target detection, Convolution, Feature extraction

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With the development of science and technology, unmanned aerial vehicle (UAV) is more and more widely used to bring a lot of convenience to the society, but also led to serious threats to public security, personal privacy, military security and other aspects. Therefore, it is increasingly important to find unknown drones quickly and accurately. In UAV detection, the technologies based on acoustic, radio and radar detection are common, but these technologies usually require expensive equipment and strict configuration. However, the method based on machine vision has the advantages of low cost and simple configuration. In addition, detection and recognition methods based on deep learning have been fully developed, but most of them are for a single visible image, and the detection and recognition effect is limited. In this paper, a fast detection and identification method based is proposed based on the backbone of YOLOv3 (You Only Look Once version3). And dual-channel detectors were used as data sources. In this method, infrared and visible images are simultaneously input into the network for feature extraction, and the extracted depth features are concatenated. Then the multi-scale prediction network is used to regression the target location to obtain the final detection and recognition results. Finally, by collecting real UAV data sets, the network is trained and tested for comparative experiments. Experimental results show that the mAP of method in this paper is worthy of improvement, and the detection speed remains at 27images/s.

Proceedings Article | 8 February 2019 Paper

Evaluation of real-time object detection model based on small targets

Ma Qi, Bin Zhu, Haidi Wang , Bo Xie, Fan Xiang, Zhengdong Cheng

Proceedings Volume 10843, 108430M (2019) https://doi.org/10.1117/12.2505930

KEYWORDS: Target detection, Image processing, Information visualization, Visualization

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In recent years, with the applications of object detection increasingly extensive, the approaches based on Deep Learning have achieved state-of-the-art performance on challenging datasets. Some researchers have made demands on real-time performance while paying attention to the accuracy of the model. In addition, with the rapid development of the object detection model, the detection of small targets has attracted extensive attention. Although several evaluations of the models have been conducted, we have conducted a more detailed evaluation of the small targets real-time detection. In this work, we carried out an in-depth evaluation of the latest real-time object detection model. We evaluate three state-of-the- art models including Single Shot MultiBox Detector (SSD), You Only Look Once version 2 (YOLO v2), and You Only Look Once version 3 (YOLO v3) with related trade-off factors i.e. accuracy, execution time and resource usage. Experiments were conducted on benchmark datasets and a newly generated dataset for small object detection. All analyses and findings are then presented.

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