为了实现复杂场景下激光跟踪仪跟踪恢复过程中合作目标靶球的检测, 本文研究了基于深度学习的靶球检测方法。首先, 分析靶球自身特点、应用环境及它在跟踪恢复过程中的作用, 然后根据Faster R-CNN模型原理与跟踪恢复应用需求提出基于超特征与浅层高分辨率特征信息复用的改进方法生成新的融合特征图, 并优化区域建议提取参数, 协同解决图像中目标多尺度变化与小尺寸导致目标漏检率高的问题; 同时提出一种基于强背景干扰的困难样本挖掘方法提高模型对外形颜色等与目标近似的干扰物识别能力, 解决模型误检测率高的问题。最后, 本文构建了目标靶球数据集并进行了对比训练与测试。测试实验结果表明: 本文提出的基于强背景干扰困难样本挖掘方法的改进Faster R-CNN模型在目标多尺度、小尺寸检测, 以及对复杂背景中相似干扰物的辨别能力都有提升, 最终对测试集的检测精度达到了90.11%, 能够满足激光跟踪仪跟踪恢复过程对合作目标靶球的视觉检测精度要求。

To visually detect a target ball for a laser tracker in a complex scene, a method of target ball detection improved by deep learning was proposed. Firstly, the characteristics of the target ball, its application environment, and its effect in tracking recovery were analyzed. Subsequently, Hypernet and shallow high-resolution features were adopted, New feature maps and an optimized region proposal were added to the original network, improving the network sensitivity to enable the detection of multi-scale and small targets. Hard example mining with strong background interference was used to reduce the ratio of error recognition, which resulted from similar objects. Finally, the dataset was established and a comparative experiment was carried out. The experiment results show that the improved method proposed in this study and hard example mining with strong background interference can increase the correct recognition rate obtained by Faster R-CNN, yielding a value of 90.11% in the test and meeting the tracking recovery requirement.