基于嵌入式设备的Anchor Free行人检测

Anchor Free Pedestrian Detection Based on Embedded Device

通过嵌入式设备在边缘端进行行人检测能满足实时、安全与隐私保护等方面的基本需求.由于原CenterNet检测网络模型backbone通常以DLA、Hourglass等复杂度较高的多层特征融合结构,嵌入式设备的计算能力有限难以满足实时的要求,因此基于BiFPN网络结构和加权特征融合方法,通过对backbone中的不同特征层进行加权融合,改进了原来的backbone方法,在保证检测精度的同时提升了检测速度.同时针对行人这一特定的检测类别,通过修改训练期间HeatMap上高斯核分布,增加对行人检测的适应性,进一步减少了因行人之间相互遮挡而漏检造成的精度降低.在Jetson TX2上的实验结果表明,改进后的行人检测AP为0.774,同时单张图像的推理时间为68 ms,能够满足在嵌入式设备上的实时要求.

Using embedded devices to detect pedestrians at the edge can meet the basic needs of real time,security and privacy protection.The original CenterNet backbone network model usually adopts Deep Layer Aggregation(DLA),Hourglass,etc.with high complexity for multi-level features fusion,which limits the computing power of embedded devices and thereby makes the real-time detection difficult.In view of this,BiFPN and weighted feature fusion are employed for the weighted fusion of feature layers in the backbone,by which the original backbone method is improved.This strategy enhances the detection speed while ensuring the detection accuracy.Further,the Gauss kernel distribution on the HeatMap during training was modified so that the adaptability to pedestrian detection can be increased.As a result,the accuracy reduction caused by missing detection due to pedestrian occlusion is lowered.The results of the experiment on Jetson TX2 show that the Average Precision(AP)of pedestrian detection with the improved method is 0.774,and the inference time of a single image is 68 ms,which can meet the requirements of embedded devices for real-time detection.