基于Faster R-CNN模型的传送带药盒与空位检测方法

制药企业为了判断传送带药盒的拥堵情况，需要对传送带上的药盒和空位进行定位，但人工方式效率低下，实时性差。在此背景下，结合Faster R-CNN模型，提出传送带目标检测方法。基于传送带图像构建模型训练集和测试集，将训练集通过ZFNet卷积神经网络计算卷积特征，并利用RPN(Region Proposal Network)生成精准的候选区域，在此基础上基于Faster R-CNN模型在候选区域上进行分类和回归，计算得到药盒与空位矩形框。通过使用测试集测试模型进行目标标注并计算出概率，结果表明，本方法对传送带目标的检测准确率良好。

A Detection Method of Medicine Box and Vacancy on Conveyor Based on Faster R-CNN Model

In order to find the congestion of the conveyor, the pharmaceutical company needs to locate the medicine boxes and vacancies on the conveyor, but the manual method is of inefficiency and has poor real-time performance. In this context, combining with the Faster R-CNN model, a target detection method is proposed. In this method, training set and testing set are constructed based on the conveyor images, then, the training set is processed through the ZFNet convolutional neural network to calculate the convolution characteristics, and the RPN (Region Proposal Network) is used to generate accurate candidate regions. On this basis, the classification and regression based on the Faster R-CNN model are performed on the candidate regions, and the rectangular boxes of the kit and the vacancy are calculated. At last, the trained model is tested by using the testing set to label the target and to calculate the probability. The result shows that the method has good detection accuracy for the conveyor belt target.