Fall Detection for Smart Living using YOLOv5

• URL: http://arxiv.org/abs/2408.15955v1
• Date: Wed, 28 Aug 2024 17:14:51 GMT
• Title: Fall Detection for Smart Living using YOLOv5
• Authors: Gracile Astlin Pereira,
• Abstract summary: This work introduces a fall detection system using the YOLOv5mu model, which achieved a mean average precision (mAP) of 0.995. The model demonstrates significant robustness and adaptability across various conditions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work introduces a fall detection system using the YOLOv5mu model, which achieved a mean average precision (mAP) of 0.995, demonstrating exceptional accuracy in identifying fall events within smart home environments. Enhanced by advanced data augmentation techniques, the model demonstrates significant robustness and adaptability across various conditions. The integration of YOLOv5mu offers precise, real-time fall detection, which is crucial for improving safety and emergency response for residents. Future research will focus on refining the system by incorporating contextual data and exploring multi-sensor approaches to enhance its performance and practical applicability in diverse environments.

Related papers

• Fall Detection for Industrial Setups Using YOLOv8 Variants [0.0]
  The YOLOv8m model, consisting of 25.9 million parameters and 79.1 GFLOPs, demonstrated a respectable balance between computational efficiency and detection performance. Although the YOLOv8l and YOLOv8x models presented higher precision and recall, their higher computational demands and model size make them less suitable for resource-constrained environments.
  arXiv  Detail & Related papers  (2024-08-08T17:24:54Z)
• YOLOv5, YOLOv8 and YOLOv10: The Go-To Detectors for Real-time Vision [0.6662800021628277]
  This paper focuses on the evolution of the YOLO (You Only Look Once) object detection algorithm, focusing on YOLOv5, YOLOv8, and YOLOv10. We analyze the architectural advancements, performance improvements, and suitability for edge deployment across these versions.
  arXiv  Detail & Related papers  (2024-07-03T10:40:20Z)
• YOLO9tr: A Lightweight Model for Pavement Damage Detection Utilizing a Generalized Efficient Layer Aggregation Network and Attention Mechanism [0.0]
  This paper proposes YOLO9tr, a novel lightweight object detection model for pavement damage detection. YOLO9tr is based on the YOLOv9 architecture, incorporating a partial attention block that enhances feature extraction and attention mechanisms. The model achieves a high frame rate of up to 136 FPS, making it suitable for real-time applications such as video surveillance and automated inspection systems.
  arXiv  Detail & Related papers  (2024-06-17T06:31:43Z)
• Precision and Adaptability of YOLOv5 and YOLOv8 in Dynamic Robotic Environments [0.0]
  This study provides a comparative analysis of YOLOv5 and YOLOv8 models. Contrary to initial expectations, YOLOv5 models demonstrated comparable, and in some cases superior, precision in object detection tasks.
  arXiv  Detail & Related papers  (2024-06-01T06:17:43Z)
• YOLO-World: Real-Time Open-Vocabulary Object Detection [87.08732047660058]
  We introduce YOLO-World, an innovative approach that enhances YOLO with open-vocabulary detection capabilities. Our method excels in detecting a wide range of objects in a zero-shot manner with high efficiency. YOLO-World achieves 35.4 AP with 52.0 FPS on V100, which outperforms many state-of-the-art methods in terms of both accuracy and speed.
  arXiv  Detail & Related papers  (2024-01-30T18:59:38Z)
• Plex: Towards Reliability using Pretrained Large Model Extensions [69.13326436826227]
  We develop ViT-Plex and T5-Plex, pretrained large model extensions for vision and language modalities, respectively. Plex greatly improves the state-of-the-art across reliability tasks, and simplifies the traditional protocol. We demonstrate scaling effects over model sizes up to 1B parameters and pretraining dataset sizes up to 4B examples.
  arXiv  Detail & Related papers  (2022-07-15T11:39:37Z)
• Inertial Hallucinations -- When Wearable Inertial Devices Start Seeing Things [82.15959827765325]
  We propose a novel approach to multimodal sensor fusion for Ambient Assisted Living (AAL) We address two major shortcomings of standard multimodal approaches, limited area coverage and reduced reliability. Our new framework fuses the concept of modality hallucination with triplet learning to train a model with different modalities to handle missing sensors at inference time.
  arXiv  Detail & Related papers  (2022-07-14T10:04:18Z)
• A lightweight and accurate YOLO-like network for small target detection in Aerial Imagery [94.78943497436492]
  We present YOLO-S, a simple, fast and efficient network for small target detection. YOLO-S exploits a small feature extractor based on Darknet20, as well as skip connection, via both bypass and concatenation. YOLO-S has an 87% decrease of parameter size and almost one half FLOPs of YOLOv3, making practical the deployment for low-power industrial applications.
  arXiv  Detail & Related papers  (2022-04-05T16:29:49Z)
• Improved YOLOv5 network for real-time multi-scale traffic sign detection [4.5598087061051755]
  We propose an improved feature pyramid model, named AF-FPN, which utilize the adaptive attention module (AAM) and feature enhancement module (FEM) to reduce the information loss in the process of feature map generation. We replace the original feature pyramid network in YOLOv5 with AF-FPN, which improves the detection performance for multi-scale targets of the YOLOv5 network.
  arXiv  Detail & Related papers  (2021-12-16T11:02:12Z)
• A high performance fingerprint liveness detection method based on quality related features [66.41574316136379]
  The system is tested on a highly challenging database comprising over 10,500 real and fake images. The proposed solution proves to be robust to the multi-scenario dataset, and presents an overall rate of 90% correctly classified samples.
  arXiv  Detail & Related papers  (2021-11-02T21:09:39Z)
• MEMO: Test Time Robustness via Adaptation and Augmentation [131.28104376280197]
  We study the problem of test time robustification, i.e., using the test input to improve model robustness. Recent prior works have proposed methods for test time adaptation, however, they each introduce additional assumptions. We propose a simple approach that can be used in any test setting where the model is probabilistic and adaptable.
  arXiv  Detail & Related papers  (2021-10-18T17:55:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.