Robotic arm target detection algorithm combined with deep learning

Existing target detection algorithms deployed on robotic arms would occupy a large amount of system resources, have poor real-time detection performance, and have a large number of model parameters. To address these problems, an improved YOLOv5 target detection algorithm was proposed. First, the ShfflenetV2 module was integrated into the YOLOv5 backbone network to replace the original focus module and cross-level local dark grid network to achieve lightweight network. Secondly, the end neck of the backbone network would have a context converter attention module with residual refinement (CTR3) module embedded into the designed backbone network to improve the feature extraction potential of the model and reduce the loss caused by sampling. Then, the head used a similarity-based adaptive message passing attention (SimAM) module to enhance the cross-scale fusion ability of features. Finally, in order to improve the bounding box regression rate and sample stability of the detection algorithm for the target, a new weighted intersection over union (WIoU) function was introduced; after the target detection network design was completed, it was deployed on the robotic arm and verified. Experimental results show that the accuracy of the improved detection algorithm in this article is 96.5%; the detection speed is 82 frames per second, which is 32 frames higher than the original algorithm; parameters is 430×104, which is 39% lower than the original algorithm; and the number of floating point calculations is 6.7 times per second, which is about 1/3 of the original number of calculations. The data shows that the improved detection algorithm has fast detection speed, small number of parameters, and small memory usage, improving detection efficiency while meeting the premise of accurate detection.