一种基于Cascade R-CNN的电子器件容器质检方法

电子器件容器生产是一种对安全性、高效性、完整性要求极高的过程，是各大企业必须要关注的问题。但是在实际的生产封装过程中，容器上的污渍、容器内的异物，外观的异常不可避免地出现，这些问题亟待解决。目前解决这些问题主要的检测方法还是人工检测和传统的机器视觉的方式，人工检测方式的缺点在于准确率高而效率低，传统机器视觉检测方式是效率高而准确率低，都难以满足高速自动化生产线要求。因此，本文提出一种基于Cascade R-CNN的电子器件容器质检方法，针对实际过程中的容器数据定向改进网络，加入Focal Loss检测难以区分的样本，使用可变形卷积更高效地提取特征，以多尺度训练方式训练强鲁棒性的模型，用于电子器件容器的多类别检测问题。实验结果表明提出的改进的基于Cascade R-CNN的电子器件容器质检模型具有高准确率和强鲁棒性。

An Electronic Device Container Quality Detection Method Based on Cascade R-CNN

The production of electronic device container is a process with high requirements for safety, efficiency and integrity, which must be paid attention to by major enterprises. But in the actual production and packaging process, the stains on the container, the foreign matters in the container, and the appearance abnormalities are inevitable. These problems need to be solved urgently. At present, the main detection methods to solve these problems are manual detection and traditional machine vision. The disadvantages of manual detection are high accuracy and low efficiency. Traditional machine vision detection methods are of high efficiency and low accuracy, which are difficult to meet the requirements of high-speed automatic production line. Therefore, this paper proposes an electronic device container quality inspection method based on cascade R-CNN. In view of the actual process of the container data oriented improvement network, we add the samples that are difficult to distinguish from Focal Loss detection, use deformable convolution to extract features more efficiently, train the strong robustness model with multi-scale training method, and apply it to the multi-category detection of electronic device containers. The experimental results show that the improved model based on cascade R-CNN has high accuracy and strong robustness.