A current trend in industries such as semiconductors and foundry is to shift their visual inspection processes to Automatic Visual Inspection (AVI) systems, to reduce their costs, mistakes, and dependency on human experts. This paper proposes a two-staged fault diagnosis framework for AVI systems. In the first stage, a generation model is designed to synthesize new samples based on real samples. The proposed augmentation algorithm extracts objects from the real samples and blends them randomly, to generate new samples and enhance the performance of the image processor. In the second stage, an improved deep learning architecture based on Faster R-CNN, Feature Pyramid Network (FPN), and a Residual Network is proposed to perform object detection on the enhanced dataset. The performance of the algorithm is validated and evaluated on two multi-class datasets. The experimental results performed over a range of imbalance severities demonstrate the superiority of the proposed framework compared to other solutions.

翻译：半导体和铸造厂等行业目前的趋势是将其视觉检查程序转向自动视觉检查系统,以减少其成本、错误和对人类专家的依赖。本文件提议了AVI系统分为两个阶段的过失诊断框架。在第一阶段,根据真实样品设计了一代模型,以合成新的样品。拟议的增强算法从真实样品中提取物体并随机混合它们,以产生新的样品,提高图像处理器的性能。在第二阶段,根据更快R-CNN、地貌金字网和残余网络改进了深层学习结构,以在强化数据集上进行对象探测。算法的性能在两个多级数据集上得到验证和评价。在一系列不平衡的分层上进行的实验结果显示了拟议框架与其他解决方案相比的优越性。