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<p>To monitor the condition of cupping spots in real-time during the operation of the automatic cupping machine, reduce the influence of the surrounding environment on the image, and improve the segmentation accuracy of the cupping spots, this paper proposes a network called MCA-Deeplabv3+. Firstly, backbone network replaced by Mobilenetv2 to reduce the model size and improve feature extraction speed; Secondly, to further enhance the network’s feature extraction capabilities, we added dilated convolution channels and integrated the CA attention mechanism into the ASPP module; Finally, data augmentation and brightness adjustment are performed on the dataset to improve the generalization of the model in different environments. The experimental results show that, in comparison with other segmentation models, MCA-Deeplabv3+performs the best in cupping spot segmentation, with mIoU and mPA reaching 93.90% and 96.73%, respectively. The practicality and effectiveness of the cupping spot segmentation model presented in this paper are thoroughly demonstrated.</p>

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MCA-Deeplabv3+: a cupping spot image segmentation network based on improved Deeplabv3+

  • 期刊论文

  • 发表日期：

    2025年1月3日

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  • Lu-Yao Ma,
  • Jian-Hua Qin,
  • Ying-Bin Liu,
  • Gui-Fen Zeng,
  • Bao-Ling Xu,
  • Ting-Ting Huang

摘要

To monitor the condition of cupping spots in real-time during the operation of the automatic cupping machine, reduce the influence of the surrounding environment on the image, and improve the segmentation accuracy of the cupping spots, this paper proposes a network called MCA-Deeplabv3+. Firstly, backbone network replaced by Mobilenetv2 to reduce the model size and improve feature extraction speed; Secondly, to further enhance the network’s feature extraction capabilities, we added dilated convolution channels and integrated the CA attention mechanism into the ASPP module; Finally, data augmentation and brightness adjustment are performed on the dataset to improve the generalization of the model in different environments. The experimental results show that, in comparison with other segmentation models, MCA-Deeplabv3+performs the best in cupping spot segmentation, with mIoU and mPA reaching 93.90% and 96.73%, respectively. The practicality and effectiveness of the cupping spot segmentation model presented in this paper are thoroughly demonstrated.