An optimized Faster R-CNN small target detection method

doi:10.13474/j.cnki.11-2246.2021.0267

Abstract

Abstract: 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.

Key words: target detection, Faster R-CNN algorithm, deep learning, image processing, novel coronavirus

CLC Number:

P237

CHENG Rui, GAO Jian, XING Qiang, SUN Zhongchang. An optimized Faster R-CNN small target detection method[J]. Bulletin of Surveying and Mapping, 2021, 0(9): 21-27.

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