A Multiscale Instance Segmentation Method Based on Cleaning Rubber Ball Images

• Main Authors: Liang, E. (Author), Su, E. (Author), Tian, Y. (Author), Wang, J. (Author), Zhang, Y. (Author)
• Format: Article
• Language: English
• Published: MDPI 2023
• Subjects: attention mechanism; Attention mechanisms; Cleaning; Cleaning system; Convolution; Feature extraction; feature fusion; Features fusions; Image enhancement; Image fusion; image segmentation; Image segmentation; Images segmentations; polarized self-attention; Polarized self-attention; Pyramid vision transformer; Pyramid Vision Transformer; Real- time; Rubber; rubber ball cleaning system; Rubber ball cleaning system; Rubber balls; Spatial reduction
• Online Access: View Fulltext in Publisher; View in Scopus

 The identification of wear rubber balls in the rubber ball cleaning system in heat exchange equipment directly affects the descaling efficiency. For the problem that the rubber ball image contains impurities and bubbles and the segmentation is low in real time, a multi-scale feature fusion real-time instance segmentation model based on the attention mechanism is proposed for the object segmentation of the rubber ball images. First, we introduce the Pyramid Vision Transformer instead of the convolution module in the backbone network and use the spatial-reduction attention layer of the transformer to improve the feature extraction ability across scales and spatial reduction to reduce computational cost; Second, we improve the feature fusion module to fuse image features across scales, combined with an attention mechanism to enhance the output feature representation; Third, the prediction head separates the mask branches separately. Combined with dynamic convolution, it improves the accuracy of the mask coefficients and increases the number of upsampling layers. It also connects the penultimate layer with the second layer feature map to achieve detection of smaller images with larger feature maps to improve the accuracy. Through the validation of the produced rubber ball dataset, the Dice score, Jaccard coefficient, and mAP of the actual segmented region of this network with the rubber ball dataset are improved by 4.5%, 4.7%, and 7.73%, respectively, and our model achieves 33.6 fps segmentation speed and 79.3% segmentation accuracy. Meanwhile, the average precision of Box and Mask can also meet the requirements under different IOU thresholds. We compared the DeepMask, Mask R-CNN, BlendMask, SOLOv1 and SOLOv2 instance segmentation networks with this model in terms of training accuracy and segmentation speed and obtained good results. The proposed modules can work together to better handle object details and achieve better segmentation performance. © 2023 by the authors.