基于YOLO轻量化的多模态行人检测算法

Lightweight multi-modal pedestrian detection algorithm based on YOLO

针对低光照环境下行人检测精度低和模型参数量大的问题,基于YOLO框架,提出一种轻量化的多模态行人检测算法EF-DEM-YOLO。采用轻量的ES-MobileNet作为主干特征提取网络,并在该网络中引入ECA和SE-ECA注意力机制模块,增强重要的通道特征,提高小目标行人的检测精度。在颈部网络中设计了基于深度可分离卷积的DBL模块,进一步缩减模型的参数量。另外,为了提高低光照条件下行人的检测精度,利用可见光模态和红外模态在不同光照条件下特征互补的特点,提出了基于图像熵的可见光与红外模态加权融合方法,并设计了融合模块EWF。相比与基准方法,该算法对于不同光照条件下的行人目标,模型的mAP提高55.5%,MR降低85.9%,模型的推理速度达到33.4帧/秒,并且均优于其他经典的目标检测算法,为边缘计算和低光照场景下的行人目标的实时检测提供了可能。

To address the problems of low accuracy in pedestrian detection and the large number of model parameters in low-light environments,a lightweight multi-modal pedestrian detection algorithm named EF-DEM-YOLO was proposed based on the YOLO framework.This algorithm employed the lightweight ES-MobileNet as the backbone feature extraction network and integrated ECA and SE-ECA attention mechanism modules in this network to enhance the important channel features,thereby elevating the detection accuracy for small-target pedestrians.A DBL module based on depth-separable convolution was also designed in the neck network to further reduce the number of parameters in the model.In addition,to improve the detection accuracy of pedestrians under low-light conditions,a weighted fusion method of visible and infrared modes based on image entropy was proposed.This method utilized the complementary features of visible and infrared modes under different lighting conditions,and the fusion module EWF is designed.In comparison to baseline methods:the proposed algorithm yielded significant improvements for pedestrian targets under different lighting conditions.The model’s mAP was increased by 55.5%,the MR was reduced by 85.9%,and the inference speed reached 33.4 frames per second,outperforming other classical object detection algorithms.This algorithm provided the possibility for real-time detection of pedestrian targets in edge computing and low-light scenes.