融合多尺度上下文卷积特征的车辆目标检测

针对现有的基于卷积神经网络的车辆目标检测算法不能有效地适应目标尺度变化、自身形变以及复杂背景等问题，提出了一种融合多尺度上下文卷积特征的车辆目标检测算法。首先采用特征金字塔网络获取多个尺度下的特征图，并在每个尺度的特征图中通过区域建议网络定位出候选目标区域，然后引入候选目标区域的上下文信息，与提取的目标多尺度特征进行融合，最后通过多任务学习联合预测出车辆目标位置和类型。实验结果表明，与多种主流检测算法相比，本算法具有更强的鲁棒性和准确性。

Vehicle detection based on fusing multi-scale context convolution features

Aiming at the problems of the existing vehicle object detection algorithm based on convolutional neural network that cannot effectively adapt to the changes of object scale, self-deformation and complex background, a new vehicle detection algorithm based on multi-scale context convolution features is proposed. The algorithm firstly used feature pyramid network to obtain feature maps at multiple scales, and candidate target regions are located by region proposal network in feature maps at each scale, and then introduced the context information of the candidate object regions, fused the context information with the multi-scale object features. Finally the multi-task learning is used to predict the position and type of vehicle object. Experimental results show that compared with many detection algorithms, the proposed algorithm has stronger robustness and accuracy.