Fast Palletizing Recognition and Grab Point Detection Based on Improved YOLOv8-Pose
Aiming at the task of recognition and grab point detection of rice bags and face bags in ware-house in palletizing scene,a lightweight fast detection algorithm model based on improved YOLOv8-Pose is proposed. Based on YOLOv8-Pose,this algorithm uses several ShuffleNetv2 modules to replace the original Darknet backbone network and reduce the model size. SimAM attention mechanism is added to improve the ability of target feature extraction. The comparison experiment shows that the model can improve the recog-nition speed without sacrificing the accuracy. The lightweight model can be applied to embedded hardware. The average accuracy of the model in the self-made data set reaches 93.7％,and the detection speed rea-ches 62 fps,which is better than the common model. The experimental results show that the model can real-ize the recognition of grab points in complex scenes. The lightweight model can be applied to embedded hardware and reduce equipment cost.
grab point detectionYOLOv8-PoseShuffleNetv2lightweight network model
万方数据
维普
