基于迁移学习的商品图像检测方法

近年来，对象识别方法被应用到多个领域.如人脸检测，车辆检测.然而模型训练所需要的边框标定需要很大的工作量.本文通过基于迁移学习的方法，将物体检测任务迁移到商品检测，且不需要边框标定.本文在分类层和边框回归层之间建立关系层，来学习两种任务之间的关联.本文建立了一个商品数据集，并提出了一种深度学习训练方法，解决了可旋转物体的检测问题.基于Faster RCNN框架，本文提出一种候选选择方法，可以在无边框标定情况下训练商品分类.本文提出的商品检测方法不需要边框标定，而且很容易训练并应用到其它数据集.

Product Image Detection Based on Transfer Learning

In recent years, object detection is transferred to other fields, for example, face and vehicle detection. However, the bounding-box labeling is a huge resources cost work. This study solves the problem that transfer object detection task to other domain dataset without bounding-box label. A relationship layer is built to learn the relationship between classification and regression task. In addition, we construct a product dataset, on which rotatable object detection is solved using our training method. A proposal selecting method is proposed for training classification based on faster RCNN framework without bounding-box label. We propose a object detection method without bounding-box annotation. The method is easy to transfer to other datasets and training.