School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China.
School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China.
Sensors (Basel). 2023 May 24;23(11):5039. doi: 10.3390/s23115039.
The primary sight control system of a tank gunner has image stabilization as one of its primary functions. The image stabilization deviation in the aiming line is a key indicator for evaluating the operational status of Gunner's Primary Sight control system. Employing image detection technology to measure image stabilization deviation enhances the effectiveness and accuracy of the detection process and allows for the evaluation of image stabilization functionality. Hence, this paper proposes an image detection method aimed at the Gunner's Primary Sight control system of a specific tank which utilizes an enhanced You Only Look Once version 5 (YOLOv5) sight-stabilizing deviation algorithm. At first, a dynamic weight factor is integrated into SCYLLA-IoU (), creating , which replaces Complete IoU () as the loss function of YOLOv5. After that, the Spatial Pyramid Pool module of YOLOv5 was enhanced to improve the multi-scale feature fusion ability of the model, thereby elevating the performance of the detection model. Finally, the C3CA module was created by embedding the Coordinate Attention (CA) attention mechanism into the CSK-MOD-C3 (C3) module. The Bi-directional Feature Pyramid (BiFPN) network structure was also incorporated into the Neck network of YOLOv5 to improve the model's ability to learn target location information and image detection accuracy. Based on data collected by a mirror control test platform, experimental results indicate an improvement in the detection accuracy of the model by 2.1%. These findings offer valuable insights into measuring the image stabilization deviation in the aiming line and facilitating the development of the parameter measurement system for Gunner's Primary Sight control system.
坦克炮手主瞄准镜控制系统的主要功能之一是具备图像稳定功能。瞄准线的图像稳定偏差是评估炮手主瞄准镜控制系统工作状态的关键指标。采用图像检测技术测量图像稳定偏差，可以提高检测过程的有效性和准确性，并能够评估图像稳定功能。因此，本文提出了一种针对特定坦克炮手主瞄准镜控制系统的图像检测方法，该方法利用改进的 You Only Look Once 版本 5（YOLOv5）稳定偏差算法。首先，将动态权重因子集成到 SCYLLA-IoU（）中，创建，它替代 Complete IoU（）作为 YOLOv5 的损失函数。然后，增强 YOLOv5 的 Spatial Pyramid Pool 模块，以提高模型的多尺度特征融合能力，从而提高检测模型的性能。最后，通过将坐标注意力（CA）注意力机制嵌入 CSK-MOD-C3（C3）模块，创建了 C3CA 模块。双向特征金字塔（BiFPN）网络结构也被纳入 YOLOv5 的 Neck 网络中，以提高模型学习目标位置信息和图像检测精度的能力。基于镜控测试平台采集的数据，实验结果表明，模型的检测精度提高了 2.1%。这些发现为测量瞄准线的图像稳定偏差和促进炮手主瞄准镜控制系统的参数测量系统的发展提供了有价值的见解。
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