Fall Detection for Industrial Setups Using YOLOv8 Variants
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Abstract (arXiv)
This paper presents the development of an industrial fall detection system utilizing YOLOv8 variants, enhanced by our proposed augmentation pipeline to increase dataset variance and improve detection accuracy. Among the models evaluated, the YOLOv8m model, consisting of 25.9 million parameters and 79.1 GFLOPs, demonstrated a respectable balance between computational efficiency and detection performance, achieving a mean Average Precision (mAP) of 0.971 at 50% Intersection over Union (IoU) across both "Fall Detected" and "Human in Motion" categories. Although the YOLOv8l and YOLOv8x models presented higher precision and recall, particularly in fall detection, their higher computational demands and model size make them less suitable for resource-constrained environments.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 9 of its 37 references that are in the map. First posted on arXiv 8 Aug 2024.
Builds on
9 of 37 references.
References · 9
- 2018YOLOv3: An Incremental Improvementcited by 2,659
- 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
- 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 982
- 2017Simple Online and Realtime Tracking with a Deep Association Metriccited by 910
- 2019Distance-IoU Loss: Faster and Better Learning for Bounding Box Regressioncited by 459
- 2020Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detectioncited by 409
- 2022YOLOv6: A Single-Stage Object Detection Framework for Industrial Applicationscited by 275
- 2023A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAScited by 151
- 2024A Comparative Analysis of YOLOv5, YOLOv8, and YOLOv10 in Kitchen Safetycited by 4
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Citing papers per year: 2024: 3.
3 papers.