Action Recognition based Industrial Safety Violation Detection

• URL: http://arxiv.org/abs/2412.05531v1
• Date: Sat, 07 Dec 2024 04:12:41 GMT
• Title: Action Recognition based Industrial Safety Violation Detection
• Authors: Surya N Reddy, Vaibhav Kurrey, Mayank Nagar, Gagan Raj Gupta,
• Abstract summary: We propose a system that employs activity recognition models to first understand the action being performed and then use object detection techniques to check for violations. This leads to a 23% improvement in the F1-score compared to the PPE-based approach on our test dataset of 109 videos.
• Score: 0.13124513975412253
• License:
• Abstract: Proper use of personal protective equipment (PPE) can save the lives of industry workers and it is a widely used application of computer vision in the large manufacturing industries. However, most of the applications deployed generate a lot of false alarms (violations) because they tend to generalize the requirements of PPE across the industry and tasks. The key to resolving this issue is to understand the action being performed by the worker and customize the inference for the specific PPE requirements of that action. In this paper, we propose a system that employs activity recognition models to first understand the action being performed and then use object detection techniques to check for violations. This leads to a 23% improvement in the F1-score compared to the PPE-based approach on our test dataset of 109 videos.

Related papers

• SH17: A Dataset for Human Safety and Personal Protective Equipment Detection in Manufacturing Industry [2.007345596217044]
  This study proposes the SH17 dataset, consisting of 8,099 annotated images containing 75,994 instances of 17 classes collected from diverse industrial environments. We have trained state-of-the-art OD models for benchmarking, and initial results demonstrate promising accuracy levels with You Only Look Once (YOLO)v9-e model variant exceeding 70.9% in PPE detection.
  arXiv  Detail & Related papers  (2024-07-05T15:40:11Z)
• Interpretable Data-driven Anomaly Detection in Industrial Processes with ExIFFI [3.7516053899419104]
  Industrial processes aim to streamline operations as much as possible, encompassing the production of the final product. In light of the emergence of Industry 5.0, a more desirable approach involves providing interpretable outcomes. This paper presents the first industrial application of ExIFFI, a recently developed approach focused on the production of fast and efficient explanations for the Extended Isolation Forest (EIF) Anomaly detection method.
  arXiv  Detail & Related papers  (2024-05-02T10:23:17Z)
• IPAD: Industrial Process Anomaly Detection Dataset [71.39058003212614]
  Video anomaly detection (VAD) is a challenging task aiming to recognize anomalies in video frames. We propose a new dataset, IPAD, specifically designed for VAD in industrial scenarios. This dataset covers 16 different industrial devices and contains over 6 hours of both synthetic and real-world video footage.
  arXiv  Detail & Related papers  (2024-04-23T13:38:01Z)
• Few-shot Detection of Anomalies in Industrial Cyber-Physical System via Prototypical Network and Contrastive Learning [5.9990208840809345]
  We propose a few-shot anomaly detection model based on prototypical network and contrastive learning. We show that the model can significantly improve F1 score and reduce false alarm rate (FAR) for identifying anomalous signals.
  arXiv  Detail & Related papers  (2023-02-21T11:09:36Z)
• Visual Detection of Personal Protective Equipment and Safety Gear on Industry Workers [49.36909714011171]
  We develop a system that will improve workers' safety using a camera that will detect the usage of Personal Protective Equipment (PPE) Our focus is to implement our system into an entry control point where workers must present themselves to obtain access to a restricted area. A novelty of this work is that we increase the number of classes to five objects (hardhat, safety vest, safety gloves, safety glasses, and hearing protection)
  arXiv  Detail & Related papers  (2022-12-09T11:50:03Z)
• Face Presentation Attack Detection [59.05779913403134]
  Face recognition technology has been widely used in daily interactive applications such as checking-in and mobile payment. However, its vulnerability to presentation attacks (PAs) limits its reliable use in ultra-secure applicational scenarios.
  arXiv  Detail & Related papers  (2022-12-07T14:51:17Z)
• An Industrial Workplace Alerting and Monitoring Platform to Prevent Workplace Injury and Accidents [0.0]
  We propose an industrial workplace alerting and monitoring platform to detect personal protective equipment (PPE) use and classify unsafe activity. Our proposed method is the first to analyze prolonged actions involving multiple people or objects. We propose the first open source annotated data set with video data from industrial workplaces annotated with the action classifications and detected PPE.
  arXiv  Detail & Related papers  (2022-10-25T06:35:00Z)
• Evaluating Machine Unlearning via Epistemic Uncertainty [78.27542864367821]
  This work presents an evaluation of Machine Unlearning algorithms based on uncertainty. This is the first definition of a general evaluation of our best knowledge.
  arXiv  Detail & Related papers  (2022-08-23T09:37:31Z)
• Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes [78.23907801786827]
  We introduce generalizations that ensure that our approach is both cost-aware and allows for fine-grained selection of examples through partially labeled scenes. Our experiments on a real-world, large-scale self-driving dataset suggest that fine-grained selection can improve the performance across perception, prediction, and downstream planning tasks.
  arXiv  Detail & Related papers  (2021-04-08T17:57:41Z)
• Anomaly Detection Based on Selection and Weighting in Latent Space [73.01328671569759]
  We propose a novel selection-and-weighting-based anomaly detection framework called SWAD. Experiments on both benchmark and real-world datasets have shown the effectiveness and superiority of SWAD.
  arXiv  Detail & Related papers  (2021-03-08T10:56:38Z)

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.