面向移动平台的深度学习复杂场景目标识别应用

A deep learning based object detection application for mobile platform in complex scenes

针对传统建筑物提取方法对人为设计的依赖,以及对建筑物边缘特征提取算法的改进,通过Keras框架获取卷积神经网络(convolutional neural networks,CNN)模型MobileNet的瓶颈层后加入新的分类器进行迁移学习,对输入图片进行大量的图像增强技术和测试集增强技术,经过三个阶段的迁移学习后获得了较高的准确率。相比其他的特征提取算法,CNN具有平移不变性以及自动提取特征等优点,在较短的时间内获得较高准确率的同时,MobileNet的权重仅有15.3 MB,兼顾计算量和精度,可以广泛移植到移动端设备。基于模型移植的移动端系统兼具拍照识别、相册识别、菜单展示等功能,为移动平台用户快速准确地判断自然场景中建筑物的信息提供了便捷工具。

Due to the presence of background noise in natural scenes and the interference of complex factors such as illumination, rotation, and shooting angle, it is very difficult to identify the image of buildings in natural scenes. Aiming at the dependence of traditional building extraction methods on human design and the improvement of building edge feature extraction algorithm.Through the Keras framework to obtain the bottleneck layer of convolutional neural networks(CNN) model MobileNet,and add a new classifier for transfer learning. A large number of data augmentation and test set augmentation are applied to the input image. After three versions of transfer learning, high accuracy was achieved within 480 iterations in three test set. Compared with other feature extraction algorithms, CNN has the advantages of non-transformation and automatic extraction of features, achieves higher accuracy in a shorter period of time. At the same time, MobileNet weight only occupy 15.3 MB with high precision and less calculation, which can be widely transplanted to mobile devices. The system based on model migration has the functions of photo recognition, photo album recognition, menu display, etc., providing mobile platform users with a convenient and simple tool to quickly and accurately obtain the information of buildings in natural scenes.