Dr. Fan-Ming Li Profile

Dr. Fan-Ming Li

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Proceedings Article | 6 November 2023 Paper

Realization of high-frequency image motion compensation mechanism based on swing-angle voice coil motor

Panwei Zhou, Xuezhuan Ding, Yang Yu, Fanming Li, Xisheng Ye

Proceedings Volume 12921, 129212W (2023) https://doi.org/10.1117/12.2691195

KEYWORDS: Mirrors, Vibration, Error analysis, Motion analysis, Finite element methods, Structural design

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According to the image motion generated in search imaging of the area-array search and tracking integrated equipment, this paper designs a high-frequency image motion compensation mechanism based on swing-angle voice coil motor, in which the high-frequency vibration of the compensating mirror is used to compensate the image motion. Based on the advantages of simple structure and control, high-frequency commutation and high-precision, a high-frequency image motion compensation mechanism driven by the swing-angle voice coil motor is proposed. This article analyses the rotary torque of the high-frequency image motion compensation mechanism, designs labyrinth seal for slewing bearing according to the requirements of high and low temperature, the life of bearing is verified by analyzing the equivalent load of bearing. The mechanical analysis of vibrating mirror is carried out by finite element simulation, and the error analysis of the rotating shaft system is also carried out, the maximum position error of image motion compensation is 13.81". The optical compensation aperture of the high-frequency image motion compensation mechanism is 60 mm. When the compensation angle is 0.3°, the frequency of vibration compensation reaches 50 Hz. The seal and mechanical requirements are considered in the structure design, and the high-frequency image motion compensation mechanism is verified by assembly and debugging.

Proceedings Article | 31 January 2023 Paper

HB-YOLOv5: improved YOLOv5 based on hybrid backbone for infrared small target detection on complex backgrounds

Xin-Yi Ye, Si-Li Gao, Fan-Ming Li

Proceedings Volume 12505, 1250505 (2023) https://doi.org/10.1117/12.2664934

KEYWORDS: Target detection, Infrared detectors, Infrared imaging, Convolution, Feature extraction, Transformers, Head

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Infrared detecting and tracking system plays an important role in national security. In order to leave enough time to intercept the unknown flying objects, the system needs to” observe” and” report” the objects as early as possible. Due to the long distance and complex background, it is hard to find and locate the small and dim targets. To tackle this difficult task, we propose a hybrid feature extraction network, taking advantages of both convolution and self-attention mechanism. Besides, we use the two-dimension Gaussian distribution to represent the bounding-box, which is convenient to measure the distance between the predicted result and the ground truth comparing to the Intersection over Union measurements. Finally, we also apply multiple data augmentation and training techniques to upgrade the detection performance. To verify effectiveness and efficiency of our method for infrared small target detection, we conduct extensive experiments on a public infrared small target dataset. The experimental results show that the model trained by our method has a significant improvement in detection accuracy and speed compared with other data-based target detection algorithms, with the average precision reaching more than 92%. The proposed method can effectively detect infrared dim-small targets in different complex backgrounds with low false alarm rate and missing alarm rate. It can also achieve outstanding performance in general small object datasets, verifying the effectiveness and robustness of our method.

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