Research on Improved Faster R-CNN in Stacked Artifact Recognition
Weiguang Han, Xuesong Han · 2023
In order to improve the sorting efficiency of workpieces, improve the sorting quality, reduce the labor intensity of workers and improve the working environment, and at the same time meet the needs of small batch, multi-variety and mixed production, this paper studies the improvement of Faster R-CNN for the field of robotic sorting. Application in stacked workpiece recognition. This paper improves the Faster R-CNN and proposes the Bin-Soft-NMS algorithm based on the Soft-NMS algorithm. The dynamic threshold is used to improve the low recall rate of the model and the low score of the adjacent stacked workpiece bounding box caused by the inaccurate threshold. the problem of inhibition. A stacked workpiece dataset was made by using four kinds of workpieces, and the model was trained on the dataset to complete the experiment. The experimental results show that the average detection accuracy of the improved model in the stacked workpiece scene is 86.26%, and the improvement in mild, moderate and severe scenarios is improved respectively. increased by 1.0%, 3.2% and 4.825%, with an average increase of 3%.