This paper aims to enhance the robustness and versatility of augmented reality (AR) tracking methods for electronic equipment assembly guidance by optimizing the structure of the position estimation network. This optimization involves integrating depthwise separable convolution with a channel attention mechanism. First, due to the lack of public datasets of 6 degrees of freedom (6-DOF) electronic equipment and various usage constraints, an RGB-D camera is used to collect and produce a 6-DOF training dataset for AR assembly guided electronic equipment. Then, using the structure of the position estimation network based on the pixel voting, depth-wise separable convolution is used to lighten the network, and the channel attention mechanism is introduced to evaluate the weight of the channels to compensate the accuracy loss caused by lightening the network. Finally, we verify the proposed network structure through AR assembly guidance by the electronic equipment task. Results show that the proposed tracking method exhibits superior robustness and maintains sound assembly guidance accuracy compared to existing method. Moreover, it can track the electronic equipment with weak texture and meet the real-time tracking requirements while ensuring accuracy.
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