改进YOLO的遮挡行人检测仿真

针对已有的YOLO(you only look once)模型在行人目标检测中对遮挡及多尺度行人易造成漏检和精度较低的问题，提出改进YOLO行人检测算法YOLO-SSC-s(YOLO-spatial pyramid poolingshuffle attention-convolutional block attention module-simplified)。修改YOLO模型骨干网络，增强跨尺度特征提取能力；在3个YOLO层前的不同位置引入空间金字塔池化模块以及空间与通道、组特征2种注意力机制，加强对不同尺度行人的特征融合；为了缓解网络模型过于复杂而降低检测性能，提高模型训练效率，根据实际情况对网络结构进行简化。实验结果表明：与YOLOv3等检测模型相比，YOLO-SSC-s可有效提高遮挡情形下中、小行人目标的检测精度、速度，降低漏检率。

Simulation of Occluded Pedestrian Detection Based on Improved YOLO

Aiming at the high missed detection rates and low accuracy of existing YOLO for occlusion and multi-scale pedestrian targets, an improved pedestrian detection algorithm is proposed. YOLO backbone is modified to enhance the capabilities of cross-scale feature extraction. To increase thepedestrian feature fusion capabilities of different scales, a spatial pyramid pooling module and two attention mechanisms are introduced at different positions in front of YOLO layers. Aiming at the detection performance degradation due to the extreme complexity of network module and to improve the model training efficiency, the network structure is pruned according to the actual situation. Experimental results show that compared with YOLOv3 etc, YOLO-SSC-s model can effectively improve the medium and small pedestrian targets detection accuracy and speed, and reduce the missed detection rates under the condition of occlusion.