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改进FCOS算法的车辆检测方法研究

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针对目前车辆检测的误差率高、检测速度慢等问题,提出一种基于改进全卷积单阶段(Fully Convolu-tional One-Stage Object Detection,FCOS)的车辆检测算法.通过引入一种考虑多个几何特征的交并比损失函数,改善了训练过程中高长宽比车辆、并行车辆难以准确回归的现象;使用多尺度卷积结合多维特征信息,增强了算法对不同尺度检测的鲁棒性;根据车辆检测场景改进了回归尺度,提高模型的推理准确度.实验结果表明,该方法在车辆检测任务中能够明显提升检测精度并保持检测速度不下降.
IMPROVED FCOS ALGORITHM FOR VEHICLE DETECTION
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.

Computer visionVehicle detectionFully convolutional networkMultiscale convolution

杜昌皓、张智

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武汉科技大学计算机科学与技术学院 湖北武汉 430065

武汉科技大学湖北省智能信息处理与实时工业系统重点实验室 湖北武汉 430065

武汉科技大学大数据科学与工程研究院 湖北武汉 430065

计算机视觉 车辆检测 全卷积网络 多尺度卷积

国家自然科学基金项目

61673304

2024

计算机应用与软件
上海市计算技术研究所 上海计算机软件技术开发中心

计算机应用与软件

CSTPCD北大核心
影响因子:0.615
ISSN:1000-386X
年,卷(期):2024.41(6)