Abstract
In the industrial field, the anthropomorphism of grasping robots is the trend of future development, however, the basic vision technology adopted by the grasping robot at this stage has problems such as inaccurate positioning and low recognition efficiency. Based on this practical problem, in order to achieve more accurate positioning and recognition of objects, an object detection method for grasping robot based on improved YOLOv5 was proposed in this paper. Firstly, the robot object detection platform was designed, and the wooden block image data set is being proposed. Secondly, the Eye-In-Hand calibration method was used to obtain the relative three-dimensional pose of the object. Then the network pruning method was used to optimize the YOLOv5 model from the two dimensions of network depth and network width. Finally, the hyper parameter optimization was carried out. The simulation results show that the improved YOLOv5 network proposed in this paper has better object detection performance. The specific performance is that the recognition precision, recall, mAP value and F1 score are 99.35%, 99.38%, 99.43% and 99.41% respectively. Compared with the original YOLOv5s, YOLOv5m and YOLOv5l models, the mAP of the YOLOv5_ours model has increased by 1.12%, 1.2% and 1.27%, respectively, and the scale of the model has been reduced by 10.71%, 70.93% and 86.84%, respectively. The object detection experiment has verified the feasibility of the method proposed in this paper.
Highlights
College of Mechanical Engineering, Guizhou University, Guiyang 550025, China; State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China; Key Laboratory of Advanced Manufacturing Technology of Ministry of Education, Guizhou University, Abstract: In the industrial field, the anthropomorphism of grasping robots is the trend of future development, the basic vision technology adopted by the grasping robot at this stage has problems such as inaccurate positioning and low recognition efficiency
The second layer of the backbone network is the CBL module, which is the smallest component in the YOLO network structure, the main component of the backbone network and the neck network, and it is mainly composed of the convolution layer, the batch normalization (BN) layer and the leaky ReLU activation function, where the number of
Compared with the original YOLOv5s, YOLOv5m and YOLOv5l models, the scale of the model has been reduced by 10.71%, 70.93% and 86.84%, respectively, indicating that the YOLOv5_ours model can guarantee the recognition accuracy, and realize the lightweight properties of the network effectively
Summary
With the rapid development of computer vision technology, computer vision tasks such as object detection and object segmentation are being widely applied in many fields of life [1,2,3,4,5,6]. Faster R-CNN was applied to medical anatomy [29] and crop identification [30] Industrial robots such as electric welding robots, picking robots [31], grasping robots [32], handling robots [33] and multi-robots [34] all use point-to-point basic vision technology. It can be seen from the above research that the object detection algorithm based on deep learning was studied more and achieved good results in the fields of transportation, medical treatment, agriculture and so on, but there is less research in the field of robot application, and more extensive and in-depth research is needed.
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