基于改进特征融合和区域生成网络的Mask R-CNN的管件分拣研究

doi:10.3969/j.issn.0255-8297.2023.05.010

Abstract

Abstract: In order to solve the problems of difficulty in distinguishing various pipe fittings and interference of light and shadow on segmentation accuracy, a pipe fittings sorting algorithm based on improved Mask R-CNN is proposed. The feature fusion network is improved by incorporating low-level feature map, improving the recognition rate of small pipe fittings. The generation box of area growth network is modified according to the size ratio of pipe fitting, so to accelerate the convergence rate of the model. Introduction of the channel and space attention module enhances the identification accuracy of pipe fitting and mask effect. The improved Mask R-CNN is applied to the sorting task of four types of pipe fittings, demonstrating increased mAP and mRecall values for mask detection (1.5% and 1.7% improvement, respectively). The robot exhibits enhanced capabilities in discriminating the location, type and size of pipe fittings, thereby meeting the accuracy requirements of sorting pipe fittings in actual production.

Key words: pipes sorting, low-level feature, area generation network, mixed attention mechanism, instance segmentation

CLC Number:

P315.69

HAN Huiyan, WU Weizhou, WANG Wenjun, HAN Xie. Research on Pipe Sorting Based on Improved Mask R-CNN Using Feature Fusion and Region Generation Network[J]. Journal of Applied Sciences, 2023, 41(5): 840-854.

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Research on Pipe Sorting Based on Improved Mask R-CNN Using Feature Fusion and Region Generation Network

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