Mr. Tianqi Zhang Profile

Tianqi Zhang

at Naval Aviation Univ.

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Proceedings Article | 5 October 2021 Paper

Application of lightweight YOLOv4 in aircraft skin fault detection

Changrui Nong, Jing Zhang, Zhenyu Liu, Qingsong Zeng, Tianqi Zhang

Proceedings Volume 11911, 119110U (2021) https://doi.org/10.1117/12.2604633

KEYWORDS: Detection and tracking algorithms, Skin, Feature extraction, Convolution, Process modeling

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Aircraft skin image fault detection plays an important role in the process of aircraft maintenance, but the traditional detection methods have low efficiency. This paper uses the YOLOv4 object detection algorithm for fault detection of aircraft skin images. Aiming at solve the problems of lightweight structure and the detection speed, we proposed the YOLOv4-ML detection algorithm. The CSPDarkNet53 backbone network in YOLOv4 is replaced by the MobileNetv3 network. The SiLU activation function is used in the shallow feature extraction network, and the adaptive training sample selection method is introduced in the training process to improve the quality of network training. The experimental results show that the YOLOV4 algorithm with lightweight design improves the detection speed by 10% while losing only 2.6% of the detection accuracy, which verifies the effectiveness of the improved algorithm.

Proceedings Article | 10 November 2020 Paper

Terminal guidance law of small anti-ship missile based on DDPG

Chenxuan Li, Boyuan Deng, Tianqi Zhang

Proceedings Volume 11584, 115841W (2020) https://doi.org/10.1117/12.2581397

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Intelligence of weapon system, precision of striking weapon and diversification of combat formulation are important developing trends in future combat operations. A terminal guidance law based on reinforcement learning (RL) and deep deterministic Policy Gradient (DDPG) is proposed to solve the problems in missile guidance system, such as heavy dependence on simulated environment training, poor interception effected by condition constraints, and insufficient guidance accuracy caused by the difference between simulated environment and the real environment. Taking advantage of the maneuverability of the small anti-ship missile relative to the ship, according to the game theory in reinforcement learning combined with the deep neural network, the optimizing trajectory is updated by analyzing the projectile motion and line of sight angle. Reward was set reasonably in line with the spatial position and the strategy gradient was optimized by using the deep neural network. Simulating experiments show that after iterative training, the DDPG guidance model for small anti-ship missiles can optimize the ballistic curve, and the miss distance can meet the requirements. Compared with traditional guidance laws, this model has better autonomous decision-making ability and strike capability.

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