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Object detection of steel surface defect based on multi-scale enhanced feature fusion
Information Sciences | 更新时间：2024-05-06
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    • Object detection of steel surface defect based on multi-scale enhanced feature fusion

    • “针对轻量级目标检测算法在钢表面缺陷检测任务中识别精度低的问题，科研人员提出了一种多尺度增强特征融合的钢表面缺陷目标检测算法。该算法创新性地采用了自适应加权融合模块，实现了深层语义与浅层细节的加权融合，有效提升了特征表示能力。同时，算法还引入了空间特征增强模块，从三个独立方向强化融合特征，增强了网络结构的稳定性，挖掘了更多的关键信息。实验结果表明，该算法的检测精度达到了80.47%，相比原始算法提升了6.81%。此外，算法的参数量和计算量也相对较小，能够快速且高精度地检测钢材表面的缺陷信息，具有很高的应用价值。这一研究成果为钢材表面缺陷检测提供了新的解决方案，也为相关领域的研究开辟了新的方向。”
      ”
    • Optics and Precision Engineering   Vol. 32, Issue 7, Pages: 1075-1086(2024)

    • DOI：10.37188/OPE.20243207.1075    

      CLC： TP394.1;TH691.9

    • Published：10 April 2024，

      Received：24 October 2023，

      Revised：01 December 2023，

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  • LIN Shanling,PENG Xueling,WANG Dong,et al.Object detection of steel surface defect based on multi-scale enhanced feature fusion[J].Optics and Precision Engineering,2024,32(07):1075-1086. DOI： 10.37188/OPE.20243207.1075.

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