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针对复杂电器零件的轻量化分类算法研究
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Abstract:
本文针对复杂电器零件的自动化检测问题，在实际工业场景下，采集并构建了具有复杂特性的(不同形状、厚度、颜色及透明度等)的多种电器零件样本数据集，并提出了轻量化的实时高精度分类模型。在模型构建中，通过引入轻量化的卷积残差块，多尺度的金字塔特征提取模块，以及显著减小模型计算量的Skip-Attention结构，使模型具有较低检测延时性的同时，并保证了较高的检测准确度。实验结果证明，本文所提出算法的实时检测效果优于较多数成熟的实时检测模型，具备应用于工业零件实时检测的可行性。
This article is based on the automation detecting of complex electrical parts. In actual industrial scenarios, varieties of electrical parts datasets with complex characteristics (different shapes, thickness, color and transparency) are collected and constructed real-time classification model. In the model construction, by introducing lightweight convolutional residual blocks, multi-scale pyramid characteristics extraction modules, and Skip-Attention structures with significantly reduced model computing, it has achieved lower detection delayed models while ensuring higher detection accuracy. The experimental results prove that the real-time detection effect of the method proposed in this article is better than the most mature real-time detection model, and it has the feasibility of real-time detection in industrial parts.
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