计算机应用与软件2024，Vol.41Issue(6)：257-262,281,7.DOI:10.3969/j.issn.1000-386x.2024.06.038
改进FCOS算法的车辆检测方法研究
IMPROVED FCOS ALGORITHM FOR VEHICLE DETECTION
摘要
Abstract
Aimed at the problems of high error rate and slow detection speed in vehicle detection,an improved fully convolutional one-stage object detection vehicle detection method is proposed.An intersection and union ratio loss function considering multiple geometric factors was introduced,which improved the phenomenon that it was difficult for high aspect ratio vehicles and parallel vehicles to regress accurately in the training process.Multiscale convolution was used to combine multi-dimensional features information,and the robustness of the algorithm to different scale detection was enhanced.According to the scene of vehicle detection,the regression scale was improved to improve the reasoning accuracy of the model.The experimental results show that this method can significantly improve the detection accuracy while maintaining the detection speed in vehicle detection tasks.关键词
计算机视觉/车辆检测/全卷积网络/多尺度卷积Key words
Computer vision/Vehicle detection/Fully convolutional network/Multiscale convolution分类
信息技术与安全科学引用本文复制引用
杜昌皓,张智..改进FCOS算法的车辆检测方法研究[J].计算机应用与软件,2024,41(6):257-262,281,7.基金项目
国家自然科学基金项目(61673304). （61673304）
