一种优化的Faster R-CNN小目标检测方法

An optimized Faster R-CNN small target detection method

图像目标检测是计算机视觉与数字图像处理的一个热门方向,其主要任务是找出图像中感兴趣的物体并确定物体的位置与类别。目前基于深度学习模型是主流的目标检测算法,利用其解决诸多学科问题成为一种趋势。本文采用区域卷积神经网络(Faster R-CNN)深度学习算法和相关图像处理算法,以ResNet50、ResNet101为骨干网络,采用特征金字塔网络开展新冠疫情期间武汉市车辆变化监测,以此分析疫情下的武汉市内部活动强度。结果显示:本文车辆目标检测方法的精确率为0.96,召回率为0.92,平均精度为0.85。疫情前(2019年11月17日)、中(2020年02月22日)车辆变化情况为:武汉汇聚中心分别为263、32辆,汪家嘴立交桥分别为89、44辆,新兴工业园分别为554、347辆,经开未来城分别为188、57辆。可知,疫情导致武汉市人口出行减少、车辆活动明显降低。

Image object detection is a popular direction in computer vision and digital image processing. Its main task is to find out the object of interest in the image and determine the location and category of the object. The current mainstream object detection algorithms are mainly based on deep learning models, and it has become a trend to solve many disciplinary problems. This article uses a method based on the combination of the regional convolutional neural network(Faster R-CNN) deep learning algorithm and related image processing algorithms, using ResNet50 and ResNet101 as the backbone network and using feature pyramid networks to monitor the changes of vehicles in Wuhan during the new crown epidemic to analyze the intensity of internal activities in Wuhan during the epidemic. The results show that the accuracy rate of the image detection method in this paper is 0.96, the recall rate is 0.915, and the average accuracy is 0.853 8.The vehicle number changes before the epidemic(November 17, 2019) and during the epidemic(February 22, 2020) is as follows: Wuhan Convergence Center(263 and 32 vehicles), Wangjiazui Overpass(89 and 44 vehicles), Xinxing Industrial Park(554 and 347 vehicles), Jingkai Future City(188 and 57 vehicles). The epidemic has led to a decrease in population travel and vehicle activities in Wuhan.