Prof. Jianxiang Xi Profile

Prof. Jianxiang Xi

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Proceedings Article | 19 February 2024 Paper

Unmanned aerial vehicles group target detection algorithm based on group structure

Wenxin Xia, Xiaogang Yang, Jianxiang Xi, Ruitao Lu, Xueli Xie

Proceedings Volume 13063, 1306328 (2024) https://doi.org/10.1117/12.3021385

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Aiming at the problem that the existing UAV group target detection algorithm does not consider the connection between group individuals and is prone to miss detection of UAV, this paper proposes a group target detection algorithm for UAV group based on group structure. Based on the group structure information, the Group Structure Relation module is constructed to improve the detection and positioning ability of UAV group targets. Experiments were carried out on the constructed dataset. The experimental results show that the mAP of the proposed UAV group target detection algorithm reaches 91.3%, which is about 2% higher than that of the original YOLOv5 algorithm, effectively improving the detection accuracy of UAV group members. At the same time, the detection speed reaches 65 frames per second, realizing the real-time detection of UAV group targets.

Proceedings Article | 18 December 2023 Paper

Spatial-temporal joint constraints based tracking method for infrared UAV

Xueli Xie, Jianxiang Xi, Ruitao Lu, Xiaogang Yang, Wenxin Xia

Proceedings Volume 12960, 129600A (2023) https://doi.org/10.1117/12.3006233

KEYWORDS: Detection and tracking algorithms, Unmanned aerial vehicles, Infrared radiation, Infrared search and track, Infrared imaging, Feature extraction, Convolution, Thermography, Motion models

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The misuse of UAVs has spurred the development of Anti-UAV technology. Infrared detector-based UAV tracking technology has become a research hotspot in the field of the Anti-UAV technology, but still faces the problem of tracking failure caused by background interference. To improve the accuracy and stability of infrared UAV tracking in the complex environments, a spatial-temporal joint constraints based infrared UAV tracking algorithm is proposed. First, a feature pyramid-based Siamese backbone is constructed to enhance the capability of feature extraction for infrared UAVs through cross-scale feature fusion. Next, a region proposal network based on spatio-temporal joint constraints is proposed. Under the constraints of template appearance features and target motion information, the location probability distribution of the infrared UAV is predicted in the entire image, and the prior anchor box is guided to focus on the candidate regions, realizing a soft adaptive search region selection mechanism. By focusing the search area, the anti-background interference capability of the local search strategy and the recapture capability of global search strategy are fused, which effectively mitigates the negative sample interference brought by global search and further enhances the discriminability of target features. Finally, the proposed algorithm is evaluated on the Anti-UAV dataset, achieving precision, success rate, and average precision of 89.5%, 64.9%, and 65.6%, respectively, with a tracking speed of 18.5 FPS. Compared with other advanced tracking algorithms, the proposed algorithm obtains better tracking performance and superior tracking performance in complex scenarios such as fast motion, thermal crossover and distractors interference.

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