Center detection methods for pulsed laser spots and cross targets in multi-optical-axis calibration
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摘要:
为提升光电吊舱多通道光轴一致性，解决光轴标校复杂成像条件下靶标中心检测精度不足的问题，提出两种具有针对性的高精度检测方法。针对脉冲激光闪烁及灼痕干扰，提出基于分阶段加权质心法的脉冲激光中心检测方法，实现大光斑粗定位到灼痕精定位的逐级优化；针对红外十字靶标边缘模糊、噪声干扰等问题，提出基于多组霍夫直线筛选与方向约束的交点优化策略，提高弱边缘条件下的中心定位精度。通过对多组激光序列及十字靶标图像进行实验验证，结果显示：脉冲激光中心检测中，分阶段加权质心法的均方根误差为0.237像素，优于最小二乘圆拟合法和传统质心法；霍夫直线筛选法在15幅红外十字靶标图像上的平均误差和最大误差仅为0.499像素和0.974像素。实验结果表明，所提方法可实现亚像素级中心检测，具有良好的稳定性以及较强的工程应用价值。
Abstract:To improve the consistency among multiple optical axes in electro-optical pods, this paper proposes two high-precision target center detection methods for optical-axis calibration under complex imaging conditions. For pulsed laser images affected by flicker and burn-mark interference, a staged weighted centroid method is proposed. This method performs coarse localization using large laser spots and then refines the center position using burn marks. For infrared cross targets with blurred edges and noise interference, an intersection optimization strategy based on Hough line screening and directional constraints is proposed. This strategy improves center localization accuracy under weak-edge conditions. Experiments were carried out using multiple laser image sequences and infrared cross-target images. The results show that the root mean square error of the staged weighted centroid method is 0.237 pixels, which is lower than those of the least-squares circle fitting method and the conventional centroid method. For 15 infrared cross-target images, the proposed Hough line screening method achieves an average error of 0.499 pixels and a maximum error of 0.974 pixels. These results demonstrate that the proposed methods can achieve sub-pixel center detection. The proposed methods also show good stability and strong potential for engineering applications.
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Key words:
- multi-optical-axis calibration /
- pulsed laser /
- cross target /
- center localization /
- visual detection
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图 1 大口径平行光管多光轴标校装置
Figure 1. Multi-optical-axis calibration setup based on a large-aperture collimator
图 4 脉冲激光中心检测过程及结果
Figure 4. Detection process and results of pulsed laser spot center localization
图 8 三种算法检测误差柱状图
Figure 8. Bar chart comparing the detection errors of the three algorithms
表 1 脉冲激光光斑中心检测结果
Table 1. Center localization results for pulsed laser spots
检测方法 单帧加权质心法 分阶段最小二乘圆拟合法 分阶段传统质心法 分阶段加权质心法 1 (957.552, 543.071) (957.389, 542.921) (957.706, 542.979) (957.549, 543.066) 2 (952.031, 586.218) (955.593, 543.174) (954.87, 543.369) (955.447, 543.356) 3 ( 1032.806 , 537.849)( 1033.310 , 538.026)( 1033.520 , 537.696)( 1032.810 , 537.841)4 (811.101, 551.459) (811.072, 550.995) (811.182, 551.06) (811.103, 551.452) 5 (806.737, 559.560) (811.046, 558.098) (811.486, 557.654) (811.360, 557.792) RMSE (pixel) / 0.256 0.292 0.237 
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表 2 十字靶标中心检测结果对比
Table 2. Comparison of cross-target center localization results
编号 参考中心 最小二乘线拟合法 行列灰度投影法 霍夫直线筛选法 1 (321.0, 259.0) (320.922, 259.102) (320.997, 258.984) (320.568, 258.945) 2 (319.0, 254.0) (319.836, 253.964) (320.287, 254.472) (319.641, 253.267) 3 (357.5, 275.5) (357.613, 275.781) (357.495, 275.933) (357.500, 275.500) 4 (167.0, 226.0) (166.813, 226.352) (167.407, 225.945) (167.00,
226.120)5 (284.0, 225.0) (284.472, 224.840) (284.485, 225.021) (283.563, 224.378) 6 (323.0, 254.5) (322.359, 255.164) (322.429, 255.046) (322.991, 254.500) 7 (303.0, 254.0) (303.385, 255.088) (303.486, 254.978) (303.099, 254.829) 8 (320.0, 259.0) (320.130, 258.848) (320.410, 258.447) (319.500, 259.308) 9 (321.0, 259.0) (321.231, 259.626) (321.458, 259.471) (320.568, 258.945) 10 (248.0, 237.5) (249.054, 237.619) (248.981, 237.460) (247.308, 237.359) 11 (440.5, 249.5) (440.826, 250.121) (440.952, 250.051) (440.273, 250.000) 12 (532.5, 217.0) (532.112, 217.571) (532.055, 217.940) (531.933, 216.875) 13 (377.5, 260.5) (377.149, 260.004) (377.449, 259.968) (377.000, 260.000) 14 (324.0, 370.0) (323.466, 370.227) (323.413, 369.575) (323.799, 369.703) 15 (318.5, 259.0) (318.519, 259.175) (318.535, 259.000) (318.068, 258.988)
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表 3 不同高斯核下的检测误差对比
Table 3. Detection error comparison under different Gaussian kernel sizes
高斯核尺寸 平均距离误差/pixel RMSE/pixel 最大误差/pixel 5×5 0.542 0.672 1.581 7×7 0.499 0.572 0.974 9×9 0.638 0.688 1.118
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