Design of Abnormal Behavior Detection System in the State Grid Business Office

Qiao, Xueming; Zhu, Wei; Guo, Dan; Jiang, Ting; Chang, Xiuli; Zhou, Yulan; Zhu, Dongjie; Cao, Ning

doi:10.1007/978-3-030-78618-2_42

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…Therefore, YOLO was not the best performance for similar objects in one image [ 83 ]. The low performance is caused by the fact that just two small boxes in the grid are anticipated and only belong to a new class of objects within the same category, resulting in an abnormal aspect ratio and other factors such as low generalization capacity [ 84 ]. Due to these reasons, we used one bounding-box, including front and rear points, to get the FPA.…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning Method for Foot Progression Angle Detection in Plantar Pressure Images

Ardhianto

Subiakto

Lin

et al. 2022

Sensors

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Foot progression angle (FPA) analysis is one of the core methods to detect gait pathologies as basic information to prevent foot injury from excessive in-toeing and out-toeing. Deep learning-based object detection can assist in measuring the FPA through plantar pressure images. This study aims to establish a precision model for determining the FPA. The precision detection of FPA can provide information with in-toeing, out-toeing, and rearfoot kinematics to evaluate the effect of physical therapy programs on knee pain and knee osteoarthritis. We analyzed a total of 1424 plantar images with three different You Only Look Once (YOLO) networks: YOLO v3, v4, and v5x, to obtain a suitable model for FPA detection. YOLOv4 showed higher performance of the profile-box, with average precision in the left foot of 100.00% and the right foot of 99.78%, respectively. Besides, in detecting the foot angle-box, the ground-truth has similar results with YOLOv4 (5.58 ± 0.10° vs. 5.86 ± 0.09°, p = 0.013). In contrast, there was a significant difference in FPA between ground-truth vs. YOLOv3 (5.58 ± 0.10° vs. 6.07 ± 0.06°, p < 0.001), and ground-truth vs. YOLOv5x (5.58 ± 0.10° vs. 6.75 ± 0.06°, p < 0.001). This result implies that deep learning with YOLOv4 can enhance the detection of FPA.

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Section: Discussionmentioning

confidence: 99%