面向多元场景的轻量级行人检测

Lightweight pedestrian detection for multiple scenes

多元场景中行人检测是当前计算机视觉领域的研究热点,尽管备受关注的深度学习能够提供很高的检测精度,但随之而来的高复杂度运算严重限制了其在可移动平台上的部署。为此,本文提出了一种面向多元场景的轻量级行人检测算法。该算法首先构建深、浅层特征融合网络以学习多尺度行人的纹理特性;然后设计了跨维特征引导注意力模块,用于保留特征提取过程中通道间、空间内的交互信息。最后基于剪枝策略去除模型中的冗余通道,以降低算法复杂度。此外,本文还设计了自适应Gamma矫正算法,以消减多元场景下光照、阴影等外界干扰对检测结果的影响。实验结果表明,本文所提方法在检测精度相当的条件下,能将模型大小压缩至10 MB,处理速度可达93 Frame/s,明显优于当前主流方法。

Currently,pedestrian detection in multiple scenes is a research hotspot in the field of computer vision.Deep learning has attracted considerable attention and can provide high detection accuracy;however,the subsequent high-complexity operations seriously limit its application on mobile platforms.To address this problem,this paper proposes a lightweight pedestrian detection algorithm for multiple scenes.Firstly,a deep and shallow feature fusion network is constructed to learn the texture features of multi-scale pedestrians.Secondly,a cross-dimensional feature-guided attention module is designed to retain the interactive information between channels and spaces in the process of feature extraction.Finally,the redundant channels in the model are trimmed using the pruning strategy,to reduce the algorithm complexity.In addition,an adaptive Gamma correction algorithm is designed to reduce the influence of external disturbances,such as illumination and shadows,on the detection results of multiple scenes.The experimental results show that the proposed method can compress the model volume to 10 MB,and the processing speed can reach 93 Frame/s while achieving similar detection accuracy,which outperforms the current mainstream methods.