A Lightweight Assembly Part Identification and Positioning Method from a Robotic Arm Perspective.

To address the problems of low precision and poor real-time performance in the process of part identification and positioning of production line assembly robotic arm, Ghost-SE YOLOv5, an assembly part identification and positioning algorithm integrating lightweight network and attention mechanism is proposed. First, the redundancy of feature map convolution is utilized, which solves the problems of large number of model parameters and floating point operations by using Ghost convolution and Ghost Bottleneck modules. Second, the attention mechanism SE Module is introduced in the backbone network to increase the propensity of feature extraction. Last, the loss function is optimized to speed up the convergence of the model. The results shows that the number of parameters, float operation per second and train time of the proposed algorithm are reduced by 45.98%, 55.99% and 24.07%, respectively. And GPU use was reduced from 7.61G to 6.43G. Furthermore, during the test the precision reached 98.6%, and the recall rate realized 95.3%. The real-time detection performance achieved 97.59 FPS, with an improvement of 34.53%. It can be seen that Ghost-SE YOLOv5 algorithm has better practicality in the part identification and positioning of robotic arm for production line assembly.