基于 yolov3 模型的复杂重叠行人目标检测网络

Yuchi Zhang
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引用次数: 0

摘要

本文基于 yolov3 模型,通过多尺度特征融合和上下文感知机制,提出了一种复杂重叠行人目标检测模型。该模型由 SONY A7R3a 摄像机在校园内拍摄,经编辑整理后得到数据集。高清视频共 358 个,分辨率为 1920*1080,帧率为 50HZ,约 17.9 万帧。通过测试,本文发现与 Single Shot Multibox Detector(SSD)相比,新提出模型的检测精度略有提高,检测精度与 Faster R-CNN 相同,新提出模型的检测精度略差于 RetinaNet。但是,Yolov3 的检测速度是 Single Shot Multibox Detector、RetinaNet 和 Faster R-CNN 的两倍多。Yolov3 的输入大小为 320*320,处理单幅图像只需要 22 毫秒,因此简化后的 Yolov3 微小图像的检测速度更快。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Complex overlapping pedestrian target detection network based on the yolov3 model
This paper proposes a complex overlapping pedestrian target detection model based on yolov3 model by multi-scale feature fusion and context-aware mechanism. The SONY A7R3a camera shot the model on campus, and the data set was obtained after editing and collating. There were 358 high-definition videos with a resolution of 1920*1080, and the frame rate was 50HZ, about 179,000 frames. Through testing, this paper finds that compared with Single Shot Multibox Detector (SSD), the detection accuracy of the newly proposed model is slightly improved, the detection accuracy is the same as that of Faster R-CNN, and the detection accuracy of the newly proposed model is slightly worse than that of RetinaNet. However, the detection speed of Yolov3 is more than twice that of Single Shot Multibox Detector, RetinaNet and Faster R-CNN. The input size of Yolov3 is 320*320, and the processing of a single image only needs 22ms, so the detection speed of the simplified Yolov3 tiny is faster.
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