The invention discloses a vehicle dense target detection method based on deep learning. The method comprises the following steps: S1, constructing and training a multi-scale dimensionality reduction convolution feature extraction network; S2, extracting a multi-scale dimensionality reduction feature map of the to-be-detected image through a multi-scale dimensionality reduction convolution feature extraction network; S3, generating a priori knowledge anchor box based on the historical image; S4, generating all target candidate areas in the to-be-detected image; And S5, carrying out ROIpooling processing on the target candidate area to obtain a detection result with a vehicle dense target. According to the invention, the characteristic of large scale difference of the dense target is considered, and on the basis of the faster-rcnn network, the thought of multi-stage multi-resolution and multi-size dimensionality reduction convolution feature extraction and shape prior-based anchor window generation is provided, so that the detection capability of a multi-scale dense model is improved, the problem of information loss in the existing related detection method is effectively solved, and automatic identification and discrimination of dense targets are realized.
本发明公开了一种基于深度学习的车辆稠密目标检测方法,包括S1、构建并训练多尺度降维卷积特征提取网络;S2、通过多尺度降维卷积特征提取网络提取待检测图像的多尺度降维特征图;S3、基于历史图像,生成先验知识锚框;S4、生成待检测图像中的所有目标候选区域;S5、对目标候选区域进行ROIpooling处理,获得具有车辆稠密目标检测结果。本发明考虑了稠密目标尺度差异大的特点,在faster‑rcnn网络的基础上,提出了多级多分辨、多尺寸降维卷积特征提取和基于形状先验的锚点窗口生成的思路,提高了对多尺度稠密模型的检测能力,有效解决了现有相关检测方法中存在的信息丢失问题,实现了对稠密目标的自动识别和辨别。
Vehicle dense target detection method based on deep learning
一种基于深度学习的车辆稠密目标检测方法
2021-06-25
Patent
Electronic Resource
Chinese
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