Detection Algorithm of Chest Bitmap Based on Spatio-temporal Context Information
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摘要:
为减小光照不均与随机抖动对胸环靶着弹检测精度的影响，该文提出一种融合时空上下文信息的胸环靶着弹检测算法。利用目标及其邻域的空间上下文信息进行光照均衡化，并提取胸环靶序列间时域运动上下文信息进行抖动校正。为提高胸环靶图像的稳定性，该算法提出多参数融合方法对抖动校正后的序列图像进行像素级融合。接着进行弹孔区域粗提取、能量筛选与重叠弹孔判别，获得弹孔位置分布。采用在部队靶场实地采集的图像进行实验，验证了该算法可以有效抑制光照不均与随机抖动带来的噪声影响，具有较好的弹孔提取能力。
Abstract:A detection algorithm based on spatio-temporal context information is proposed to reduce the influence of non-uniform illumination and random jitter on the accuracy of target hole detection. The light equalization is carried out by using the spatial context information of target and its neighborhood, and the temporal motion context information between chest bitmap sequences is extracted for dithering correction. In order to improve the stability of chest bitmaps, a multi-parameter fusion method is proposed to perform pixel-level fusion of jitter corrected sequence images. Then, rough extraction of bullet hole area, energy screening and overlapping bullet holes discrimination are carried out to obtain the location distribution of bullet holes. The experimental results show that the algorithm can effectively suppress the noise caused by non-uniform illumination and random jitter, and has great ability of bullet hole extraction.
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表 1 IQA参数比较
${\mu _1}$(mean) ${\mu _2}$(std) ${\mu _3}$(grad) SSEQ 0.1 0.4 0.5 49.7565 0.2 0.3 0.5 50.7473 0.3 0.2 0.5 50.5672 0.4 0.1 0.5 49.4026 0.1 0.3 0.6 49.2834 0.2 0.2 0.6 50.0364 0.3 0.1 0.6 49.9368 0.1 0.2 0.7 49.2470 0.2 0.1 0.7 49.3499 0.1 0.1 0.8 48.9924 
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表 3 弹孔中心坐标位置偏差与检测效率
序号 真值 检测结果 $(|\Delta x|,|\Delta y|)$ $\sqrt {\Delta {x^2} + \Delta {y^2}} $ 检测效率(s) 1 (206,725) (206.50,725.00) (0.50,0) 0.50 0.7413 2 (460,297) (459.63,297.50) (0.37,0.50) 0.62 0.7744 3 (267,541) (266.35,542.24) (0.65,1.24) 1.40 0.8000 4 (436,456) (436.77,457.13) (0.77,1.13) 1.37 0.7711 5 (733,626) (733.00,626.49) (0,0.49) 0.49 0.7656 6 (685,891) (686.50,888.50) (1.50,2.50) 2.92 0.7711 7 (797,915) (796.50,915.50) (0.50,0.50) 0.71 0.7586 8 (573,821) (573.00,819.50) (0,1.50) 1.50 0.7843 9 (700,758) (699.00,758.00) (1.00,0) 1.00 0.7946 10 (760,711) (760.13,710.32) (0.13,0.68) 0.69 0.8425
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