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.