基于YOLOv2箕斗结构的煤矿井下行人检测

2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC) Pub Date : 2017-10-01 DOI:10.1109/ITOEC.2017.8122550

Lin Wang, Weishan Li, Yuliang Zhang, Chen Wei

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引用次数: 10

摘要

行人检测是目标检测中的一个重要课题。与其他目标检测器相比，YOLOv2在一般目标检测中精度高、速度快，但在检测拥挤行人时精度下降。本文结合FCN的箕斗结构，对YOLOv2网络进行了定制，以提高对煤矿井下成群出现的小行人的检测精度。因此，我们提出了YOLOv2的两个修改版本:YWSSv1和YWSSv2。与YOLOv2相比，YWSSv1略微提高了0.1 mAP，但保持了相同的快速速度。YWSSv2比YOLOv2显著提高了12个mAP，但牺牲了只有5 FPS的速度。

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Pedestrian detection based on YOLOv2 with skip structure in underground coal mine

Pedestrian detection is an important topic in object detection. Compared with other object detectors, YOLOv2 achieves high accuracy and fast speed for general object detection, however it degrades accuracy when detecting crowed pedestrians. In this paper, combining with the skip structure of FCN, we tailor the YOLOv2 network to improve the accuracy in detecting small pedestrians which appear in groups in underground coal mine. In this way, we propose two modified versions of YOLOv2 which are YWSSv1 and YWSSv2. Compared with YOLOv2, YWSSv1 slightly improves 0.1 mAP but keeps the same fast speed. YWSSv2 significantly gains 12 mAP higher than YOLOv2 but sacrifices its speed at just 5 FPS.

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来源期刊

2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC)

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