基于改进Faster RCNN与Grabcut的商品图像检测

近年来，图像检测方法已经被应用于很多领域.然而，这些方法都需要在目标任务上进行大量边框标注数据的重新训练.本文基于Faster RCNN方法，并对其进行改进，解决了在小数据且无需边框标注的情况下的商品图像检测问题.首先对Faster RCNN的边框回归层进行改进，提出了一种非类别特异性的边框回归层，仅使用公开数据集训练，无需在目标数据集上进行再训练，并将其用于数据预标定与商品检测.然后结合Grabcut与非类别特异性Faster RCNN提出了一种样本增强方法，用来生成包含多个商品的训练图像；并为Faster RCNN添加了重识别层，提高了检测精度.

Product Image Detection Method Based on Improved Faster RCNN and Grabcut

In recent years, object detection has been applied to many fields. However, retraining using large amount of bounding-box labeled data is needed. This study improves the Faster RCNN method and solves the problem of detecting multi-object in images using few-shot single object training data without bounding-box annotation. We propose a non-classwise bounding-box regression layer, which is only trained by public dataset and used for product training image labeling and testing image detection. Combined with Grabcut method, a data augmentation method is proposed to generate multi-object product training image. The improved faster RCNN model is re-trained by these images. In addition, a re-identification layer is added to the Faster RCNN architecture and improves the detection performance.