Detection of human lower limb mechanical axis key points and its application on patella misalignment detection

Zhang, Yueming; Zhang, Guoshan; Guan, Bin; Yao, Jinkun

doi:10.1007/s10489-021-02718-3

Cited by 1 publication

(2 citation statements)

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“…Four indices, including the NE and PCK, were used to evaluate the performance of the developed hourglass model. Te NE and PCK are typical evaluation indicators for the solutions of most key point problems, such as facial key point estimation [41] and human posture estimation based on key points [31]. When the NE and PCK were calculated, a distance normalization parameter had to be used because the area occupied by a bolt difered between images.…”

Section: Model Evaluationmentioning

confidence: 99%

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Detection of the Bolt Loosening Angle through Semantic Key Point Extraction Detection by Using an Hourglass Network

Shichuan

Zhao

2023

Structural Control and Health Monitoring

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Damage in bolts, which are used as connecting fasteners in steel structures, affects structural safety. Sophisticated machine vision methods have been formulated for the detection of loose bolts, but their accuracy remains an area for improvement. In this paper, a method based on a stacked hourglass network is proposed for automatically extracting the key points of a bolt and for obtaining the bolt loosening angle by comparing the rotations of the key points before and after the bolt is damaged. A data set containing 100 images of key bolt loosening points was collected, and rotation was performed as data augmentation to yield 1800 images. Moreover, a method was designed for automatically annotating the augmented image data set. In this study, 70%, 10%, and 20% of the annotated image data set were used for training, validation, and testing, respectively. Subsequently, a neural network model based on a stacked hourglass network was established to train the annotated image data set. The detection results were evaluated in terms of normalized errors (NEs), percentage of correct key points (PCK), detection speed, and training time. In testing, the proposed method accurately and efficiently identified the bolt loosening angle, with a PCK value as high as 99.3%. The accuracy of the proposed method was also highly robust to different shooting distances, viewing angles, and illumination levels.

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Section: Model Evaluationmentioning

confidence: 99%

“…Stacked hourglass networks [30] have achieved high performance in applications such as human posture estimation [31], vehicle positioning and recognition [32], construction robots [33], and construction site safety maintenance [34]. Tese networks can be used to fnd the contours of a detected object accurately and rapidly.…”

Section: Introductionmentioning

confidence: 99%

Detection of the Bolt Loosening Angle through Semantic Key Point Extraction Detection by Using an Hourglass Network

Shichuan

Zhao

2023

Structural Control and Health Monitoring

View full text Add to dashboard Cite

show abstract

Detection of human lower limb mechanical axis key points and its application on patella misalignment detection

Cited by 1 publication

References 38 publications

Detection of the Bolt Loosening Angle through Semantic Key Point Extraction Detection by Using an Hourglass Network

Detection of the Bolt Loosening Angle through Semantic Key Point Extraction Detection by Using an Hourglass Network

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