Mr. Zefeng Yin Profile

Zefeng Yin

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Proceedings Article | 1 April 2024 Paper

Research on biomimetic hexapod unmanned combat platform based on machine vision

Zefeng Yin, Zhongxin Li, Jinxiao Xu

Proceedings Volume 13082, 130820K (2024) https://doi.org/10.1117/12.3026206

KEYWORDS: Target detection, Robots, Biomimetics, Chest, Control systems, Machine vision, Detection and tracking algorithms, Target recognition, Object detection, Fire

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In response to the requirements for target identification and tracking in battlefield scenarios, a design has been developed that integrates the control methodologies of hexapod robots with the principles of machine vision. This encompasses the creation of a compact, biomimetic hexapod unmanned combat robot that is based on machine vision principles. The core approach involves the utilization of a high-frame-rate wide-angle camera mounted on the hexapod robot to perform target recognition and tracking on chest ring targets. This is achieved by employing the YOLOv5 and Deepsort algorithms for target detection and tracking, allowing the acquisition of target centroid coordinates. These coordinates are then transmitted to an Arduino development board to govern the hexapod robot's movement. Furthermore, the system has been augmented with an electronic ignition module and firing mechanism, enabling precise firepower upon target locking. By effectively integrating machine vision and hexapod robot motion control technologies, this system provides a comprehensive exposition of the detailed implementation of automated target engagement.

Proceedings Article | 19 February 2024 Paper

Video anomaly detection via improved future frame prediction

Bo Li, Zhongxin Li, Zefeng Yin

Proceedings Volume 13063, 130630I (2024) https://doi.org/10.1117/12.3021297

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Video anomaly detection is extensively utilized across a variety of domains including public transportation, industrial production, city management, and military fields to mitigate risks and bolster enhance safety. To tackle the challenges associated with video anomaly detection in intricate environments, we propose a light but efficient framework that builds upon future frame prediction techniques. Our framework incorporates Convolutional Long Short-Term Memory (ConvLSTM), masked convolution, and attention mechanisms to enhance the detection accuracy. Furthermore, to simplify the model's complexity, we replace the convolutional layers in the network with depthwise separable convolutions (DSC). Through evaluation on public datasets such as CUHK Avenue, UCSD Peds1, and UCSD Peds2, our proposed network model exhibits both high accuracy and real-time performance.

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