基于双模全卷积网络的行人检测算法(特邀)

在近距离行人检测任务中, 平衡算法的检测精度与检测速度对于检测算法的实际应用有着重要意义。为了快速并准确地检测出近景行人目标, 提出了一种基于模型融合全卷积网络的行人检测算法。首先,通过全卷积检测网络对图像中的目标进行检测, 得到一系列候选框; 其次, 通过弱监督训练的语义分割网络得到图像的像素级分类结果; 最后, 将候选框与像素级分类结果融合, 完成检测。实验结果表明: 算法在检测速度与精度方面都具有较高的性能。

Abstract

In the task of close range pedestrian detection, the balance of the precision and speed were of great significance to the practical application of the detection algorithm. In order to detect the close range target quickly and accurately, a pedestrian detection algorithm based on fused fully convolutional network was proposed. Firstly, a fully convolutional detection network was used to detect the target in the image, and a series of candidate bounding boxes were obtained. Secondly, pixel level classification results of the image were obtained by using a semantic segmentation network with weakly supervised training. Finally, the candidate bounding boxes and the pixel level classification results were fused to complete the detection. The experimental results show that the algorithm has good performance in both the speed and the precision of detection.

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罗海波, 何淼, 惠斌, 常铮. 基于双模全卷积网络的行人检测算法(特邀)[J]. 红外与激光工程, 2018, 47(2): 0203001. Luo Haibo, He Miao, Hui Bin, Chang Zheng. Pedestrian detection algorithm based on dual-model fused fully convolutional networks(Invited)[J]. Infrared and Laser Engineering, 2018, 47(2): 0203001.

基于双模全卷积网络的行人检测算法(特邀)

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