Research on pipe fitting identification and pose estimation based on deep learning
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摘要: 管件的6D位姿估计是机器人抓取抛光的前提，传统的估计策略工作量大，基于深度学习提出改进的深度对象姿态估计（deep object pose estimation，DOPE）框架对管件实时检测。首先，制作合成数据训练网络。其次，对网络提出改进：针对管件的旋转对称性，自定义损失函数，提高管件检测精度；且采用Resnet18提取管件特征，减轻网络规模。最后，探究热图阶段数对推理时间的影响。改进后的DOPE网络估计管件位姿时，其精度-阈值曲线下面积（area under the curve，AUC）提高了17%，参数量和浮点计算量分别减少9%和20%，检测单张图片仅需102 ms。估计管件位姿试验证明了改进DOPE的有效性，且满足工业要求。Abstract: The 6D pose estimation of pipe fitting is the premise of robot grasping and polishing.Heavy workload is required for traditional estimation strategies.Based on deep learning,an improved deep object pose estimation (DOPE) framework was proposed for real-time detection pipe fitting. First of all, a synthetic dataset was created to train the network. In the next place, some suggestions were put forward: in order to solve the symmetry of pipe fitting and improve the detection accuracy of pipe fitting, a custom loss function was proposed. The network size has been reduced by using resnet18 to extract pipe fitting features. In the end, the effect of the number of heatmap stages on reasoning time was explored. The AUC is 17% higher, the number of parameters and floating point operations are 9% and 20% lower, and it only takes 102 ms to detect a picture when using the improved DOPE to estimate the pipe fitting pose. The effectiveness of modified DOPE was proved by the test of estimated pipe fitting pose, and it also meet the industrial requirements.
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Key words:
- pipe fitting /
- pose estimation /
- DOPE /
- deep learning
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表 1 Resnet18前13层网络结构
模块名 卷积1 卷积2 卷积3 卷积4 残差
模块7$ \times $7, 64,
S = 2$ \left\{\begin{array}{c}3\times 3，64\\ 3\times 3，64\end{array}\right\}\times 2 $ $ \left\{\begin{array}{c}3\times 3，128\\ 3\times 3，128\end{array}\right\}\times 2 $ $ \left\{\begin{array}{c}3\times 3，256\\ 3\times 3，256\end{array}\right\}\times 2 $ 输出
尺寸200 $ \times $ 200 200 $ \times $ 200 100 $ \times $ 100 50$ \times $ 50 
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表 2 不同特征提取模块试验结果
AUC 参数量/M FLOPs/G 推理速度/ms H62黄铜管 活塞杆 Vgg19 0.46 0.49 50.26 155.65 248 Resnet18 0.49 0.48 46.00 124.90 203 Resnet34 0.50 0.50 51.39 145.58 241
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表 3 采用resnet18提取特征时不同阶段数试验结果
10 000张
（数据集大小）AUC 参数
量/MFLOPs/G 推理
时间/msH62黄
铜管活塞杆 Resnet 18 + 2个阶段 0.46 0.46 12.48 41.09 102 4个阶段 0.49 0.45 29.24 82.99 155 6个阶段 0.49 0.48 46.00 124.90 203
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