融合多尺度信息和跨维特征引导的轻量行人检测

Lightweight pedestrian detection based on multi-scale information and cross-dimensional feature guidance

摘要:

针对复杂道路场景下行人检测精度与速度难以提升的问题,提出一种融合多尺度信息和跨维特征引导的轻量级行人检测算法。首先以高性能检测器YOLOX为基础框架,构建多尺度轻量卷积并嵌入主干网络中,以获取多尺度特征信息。然后设计了一种端到端的轻量特征引导注意力模块,采用跨维通道加权的方式将空间信息与通道信息融合,引导模型关注行人的可视区域。最后为减少模型在轻量化过程中特征信息的损失,使用增大感受野的深度可分离卷积构建特征 融合网络。实验结果表明,相比于其他主流检测算法,所提算法在KITTI数据集上达到了71.03%的检测精度和80 FPS的检测速度,在背景复杂、密集遮挡、尺度不一等场景中都具有较好的鲁棒性和实时性。

Abstract:

Aiming at the detection accuracy and speed of pedestrian detection in complex road environment,a lightweight pedestrian detection algorithm based on multi-scale information and cross-dimensional feature guidance is proposed.Firstly,based on the high-performance detector YOLOX,a multi-scale lightweight convolution is constructed and embedded in the backbone network to obtain multi-scale feature information. Secondly, an end-to-end lightweight feature guided attention module is designed,which guides the model to focus on the visible region of pedestrian targets by fusing spatial information and related information through cross-dimensional channel weighting mehod. Finally,in order to reduce the loss of feature information in the process of lightweight of the model,a feature fusion network is constructed by depthwise separable convolution with increasing the depth of the receptive field.The experimental results show that compared with other mainstream detection algorithms,the proposed algorithm on the KITTI dataset reaches 71.03% detection accuracy and 80 FPS detection speed,which has better robustness and real-time performance in scenes with complex background,dense occlusion and different scales.