Research on Human Posture Estimation Algorithm Based on YOLO-Pose

Ding, Jing; Niu, Shanwei; Nie, Zhigang; Zhu, Wenyu

doi:10.3390/s24103036

Cited by 4 publications

(1 citation statement)

References 37 publications

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“…Although the above detectors show significant improvements, the dependence on region proposals increases the computational overhead, prompting research into region proposal-free detectors. A major innovation occurred with the advent of YOLO [ 19 , 32 , 33 ]. YOLO divides an image into a grid and predicts bounding boxes using the class score of each cell to achieve unprecedented speed and accuracy.…”

Section: Related Workmentioning

confidence: 99%

Proposal-Free Fully Convolutional Network: Object Detection Based on a Box Map

Su,

Adam,

Nasrudin

et al. 2024

Sensors

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Region proposal-based detectors, such as Region-Convolutional Neural Networks (R-CNNs), Fast R-CNNs, Faster R-CNNs, and Region-Based Fully Convolutional Networks (R-FCNs), employ a two-stage process involving region proposal generation followed by classification. This approach is effective but computationally intensive and typically slower than proposal-free methods. Therefore, region proposal-free detectors are becoming popular to balance accuracy and speed. This paper proposes a proposal-free, fully convolutional network (PF-FCN) that outperforms other state-of-the-art, proposal-free methods. Unlike traditional region proposal-free methods, PF-FCN can generate a “box map” based on regression training techniques. This box map comprises a set of vectors, each designed to produce bounding boxes corresponding to the positions of objects in the input image. The channel and spatial contextualized sub-network are further designed to learn a “box map”. In comparison to renowned proposal-free detectors such as CornerNet, CenterNet, and You Look Only Once (YOLO), PF-FCN utilizes a fully convolutional, single-pass method. By reducing the need for fully connected layers and filtering center points, the method considerably reduces the number of trained parameters and optimizes the scalability across varying input sizes. Evaluations of benchmark datasets suggest the effectiveness of PF-FCN: the proposed model achieved an mAP of 89.6% on PASCAL VOC 2012 and 71.7% on MS COCO, which are higher than those of the baseline Fully Convolutional One-Stage Detector (FCOS) and other classical proposal-free detectors. The results prove the significance of proposal-free detectors in both practical applications and future research.

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Section: Related Workmentioning

confidence: 99%

Proposal-Free Fully Convolutional Network: Object Detection Based on a Box Map

Su,

Adam,

Nasrudin

et al. 2024

Sensors

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Embodied Movement and Generative Media Performance

Wendell,

Guan

2024

SIGGRAPH Asia 2024 Art Papers

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Research on Fault Detection of Belt Conveyor Drum Based on Improved YOLOv8 Network Mode

Du,

Yu,

Wang

et al. 2024

Preprint

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This paper presents a comprehensive study on enhancing the accuracy, real-time performance, and reliability of fault detection in conveyor belt drums. Leveraging insights from two distinct approaches, a novel lightweight network model, YOLOv8n + EMBC + SCC, is proposed. The model integrates the strengths of YOLOv8n in target detection accuracy and speed with innovative modules designed for improved performance. Firstly, the EMBC module, based on DSC high-efficiency convolution, replaces the traditional C2F module in the backbone and neck segments, resulting in a notable 14.5% increase in speed and a 0.7% enhancement in accuracy. Secondly, the SCC efficient convolution module replaces the Conv module in the detection head, further optimizing computational load and model performance, leading to an additional 11.73% increase in speed and a 0.7% improvement in accuracy. Experimental results demonstrate the efficacy of the proposed model, achieving a detection accuracy of 93.4%, surpassing YOLOv8n by 0.9%. Moreover, the model exhibits an improved Frames Per Second (FPS) value of 38.21, representing a 3.56 f/s advancement over YOLOv8n. Heatmap analysis validates the model's superiority in terms of high detection accuracy, precise fault identification, and clear fault localization. This research contributes to the development of a fast, precise, and reliable fault detection system suitable for conveyor belt drum applications, with implications for improving operational efficiency and maintenance practices in industrial settings.

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Research on Human Posture Estimation Algorithm Based on YOLO-Pose

Cited by 4 publications

References 37 publications

Proposal-Free Fully Convolutional Network: Object Detection Based on a Box Map

Proposal-Free Fully Convolutional Network: Object Detection Based on a Box Map

Embodied Movement and Generative Media Performance

Research on Fault Detection of Belt Conveyor Drum Based on Improved YOLOv8 Network Mode

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