基于并行差分进化–梯度特征深度森林的废旧手机识别方法

Used mobile phone recognition method based on parallel differential evolution and gradient feature deep forest

废旧电子产品“互联网+回收”模式的推广,使得无人化、智能化的废旧手机(UMP)回收装备成为典型城市固体废物资源化领域的重点关注对象.本文以基于回收装备的UMP智能化识别组件为研究对象,设计并实现了一种基于并行差分进化(PDE)–梯度特征深度森林(GfDF)算法的UMP识别方法.本方法由UMP识别模型和PDE参数寻优模型组成,其中:前者包含的UMP定位裁剪模块基于Faster–RCNN模型对图像裁剪以获得有效信息, GfDF识别模块通过引入多尺度梯度特征策略使其更易学习“定位模块”抓取信息;后者使用并行策略优化GfDF模型超参数以提高UMP识别精度.实验结果表明,相比于深度模型和其他机器学习模型,本方法在识别精度和训练时间上均具有优势,能够有效提高回收装备自动化程度和手机回收效率.

The promotion of the “Internet + Recycling” model of waste electronic products has made unmanned and intelligent used mobile phone(UMP) recycling equipment become the focus of attention in the field of typical urban solid waste recycling. This papper takes used mobile phone recognition(UMPR) based on recycling equipment as the research object. We design and implement a UMPR method based on parallel differential evolution(PDE)-gradient feature deep forest(GfDF) algorithm. This method is composed of the UMPR model and the PDE parameter optimization model. The mobile phone positioning and cropping module included in the former is based on the Faster–RCNN model and crops the image to obtain the effective information. The GfDF recognition module introduces a multi-scale gradient feature strategy to make it easier to learn the “location module” for capturing information. The PDE parameter optimization module uses a parallel strategy to optimize the hyperparameters of the GfDF model to improve the accuracy of the UMP identification. The experimental results show that compared with deep models and other machine learning models, this method has performance advantages in recognition accuracy and training time. It can effectively improve the degree of automation of recycling equipment and the efficiency of mobile phone recycling.