一种改进YOLOv3的学校场所目标识别方法

School place identification based on improved YOLOv3

基于遥感影像进行特定场所类型的识别在智慧城市规划、土地利用分析、平安城市建设等多方面都具有重要意义。然而,不同场所的环境景观属性(如道路和停车场等)比较复杂,难以用传统的分类或目标识别方法基于简单的规则进行识别。卷积神经网络具有较强的空间信息挖掘能力,尝试对著名的YOLOv3模型进行改进,提出一种名为YOLO-S-CIoU的新模型,用于学校场所目标的识别。主要改进工作包括:1)使用SRXnet模块替换YOLOv3中的Darknet53模块以提高特征学习能力;2)利用complete-IoU loss(CIoU loss)优化边界框的回归;3)基于自制的学校场所样本数据集(SS数据集)进行训练和验证。实验结果表明,YOLO-S-CIoU的平均精度(AP)达到96.46%;参数量为226 MB。与改进前YOLOv3相比,YOLO-S-CIoU实现了参数量9 MB的下降以及AP 2.3%的提升。此外,在新疆图木舒克市和烟台市区域遥感影像中对学校场所目标识别,召回率比YOLOv3分别提高37.5%和42.2%。这表明改进后的网络模型在不同地理区域的遥感影像识别中具有更强的鲁棒性和更高的识别能力。

The specific place identification based on remote sensing images is of great significance in smart city planning,land use analysis,and safe city construction.However,it is difficult to identify these places with simple rules by using traditional classification or target recognition methods due to the complex environmental background.Convolutional neural networks have strong spatial information mining capabilities.In this article,we improved the well-known YOLOv3 model to a new model called YOLO-S-CIoU for school place identification.The main improvements include:1)Using the SRXnet module to replace the Darknet53 module in YOLOv3 to improve the feature learning ability;2)Using complete-IoU loss(CIoU loss)to optimize the regression of the bounding box;3)Training and verification based on self-made school place sample dataset(SS dataset).The results showed that the average accuracy(AP)of YOLO-S-CIoU reached 96.46%;the parameter amount was 226 MB.Compared with YOLOv3,YOLO-S-CIoU has achieved a 9 MB reduction in parameter volume and a 2.3%increase in AP.In addition,in the regional remote sensing images of Tumshuk and Yantai,the recall rates of school places were increased by 37.5%and 42.2%than YOLOv3,respectively.These works show that the improved model has stronger robustness and higher identification ability in remote sensing image identification in different areas.