Image-Based Fire Detection in Industrial Environments with YOLOv4

• URL: http://arxiv.org/abs/2212.04786v1
• Date: Fri, 9 Dec 2022 11:32:36 GMT
• Title: Image-Based Fire Detection in Industrial Environments with YOLOv4
• Authors: Otto Zell, Joel P{\aa}lsson, Kevin Hernandez-Diaz, Fernando Alonso-Fernandez, Felix Nilsson
• Abstract summary: This work looks into the potential of AI to detect and recognize fires and reduce detection time using object detection on an image stream. To our end, we collected and labeled appropriate data from several public sources, which have been used to train and evaluate several models based on the popular YOLOv4 object detector.
• Score: 53.180678723280145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fires have destructive power when they break out and affect their surroundings on a devastatingly large scale. The best way to minimize their damage is to detect the fire as quickly as possible before it has a chance to grow. Accordingly, this work looks into the potential of AI to detect and recognize fires and reduce detection time using object detection on an image stream. Object detection has made giant leaps in speed and accuracy over the last six years, making real-time detection feasible. To our end, we collected and labeled appropriate data from several public sources, which have been used to train and evaluate several models based on the popular YOLOv4 object detector. Our focus, driven by a collaborating industrial partner, is to implement our system in an industrial warehouse setting, which is characterized by high ceilings. A drawback of traditional smoke detectors in this setup is that the smoke has to rise to a sufficient height. The AI models brought forward in this research managed to outperform these detectors by a significant amount of time, providing precious anticipation that could help to minimize the effects of fires further.

Related papers

• Hardware Acceleration for Real-Time Wildfire Detection Onboard Drone Networks [6.313148708539912]
  wildfire detection in remote and forest areas is crucial for minimizing devastation and preserving ecosystems. Drones offer agile access to remote, challenging terrains, equipped with advanced imaging technology. limited computation and battery resources pose challenges in implementing and efficient image classification models. This paper aims to develop a real-time image classification and fire segmentation model.
  arXiv  Detail & Related papers  (2024-01-16T04:16:46Z)
• Fire Detection From Image and Video Using YOLOv5 [0.0]
  An improved YOLOv5 fire detection deep learning algorithm is proposed. Fire-YOLOv5 attains excellent results compared to state-of-the-art object detection networks. When the input image size is 416 x 416 resolution, the average detection time is 0.12 s per frame.
  arXiv  Detail & Related papers  (2023-10-10T06:37:03Z)
• Trash to Treasure: Low-Light Object Detection via Decomposition-and-Aggregation [76.45506517198956]
  Object detection in low-light scenarios has attracted much attention in the past few years. A mainstream and representative scheme introduces enhancers as the pre-processing for regular detectors. In this work, we try to arouse the potential of enhancer + detector.
  arXiv  Detail & Related papers  (2023-09-07T08:11:47Z)
• Deep Industrial Image Anomaly Detection: A Survey [85.44223757234671]
  Recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD) In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques. We highlight several opening challenges for image anomaly detection.
  arXiv  Detail & Related papers  (2023-01-27T03:18:09Z)
• Multimodal Wildland Fire Smoke Detection [5.15911752972989]
  Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the U.S. We present our work on integrating multiple data sources in SmokeyNet, a deep learning model usingtemporal information to detect smoke from wildland fires. With a time-to-detection of only a few minutes, SmokeyNet can serve as an automated early notification system, providing a useful tool in the fight against destructive wildfires.
  arXiv  Detail & Related papers  (2022-12-29T01:16:06Z)
• Image-based Early Detection System for Wildfires [2.8494271563126676]
  Wildfires are a disastrous phenomenon which cause damage to land, loss of property, air pollution, and even loss of human life. We present our Wildfire Detection and Alert System which use machine learning to detect wildfire smoke with a high degree of accuracy. Our technology is currently being used in the USA to monitor data coming in from hundreds of cameras daily.
  arXiv  Detail & Related papers  (2022-11-03T07:38:30Z)
• Adversarially-Aware Robust Object Detector [85.10894272034135]
  We propose a Robust Detector (RobustDet) based on adversarially-aware convolution to disentangle gradients for model learning on clean and adversarial images. Our model effectively disentangles gradients and significantly enhances the detection robustness with maintaining the detection ability on clean images.
  arXiv  Detail & Related papers  (2022-07-13T13:59:59Z)
• FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection [0.0]
  Fire Ignition Library (FIgLib) is a publicly-available dataset of nearly 25,000 labeled wildfire smoke images. SmokeyNet is a novel deep learning architecture usingtemporal information from camera imagery for real-time wildfire smoke detection. When trained on the FIgLib dataset, SmokeyNet outperforms comparable baselines and rivals human performance.
  arXiv  Detail & Related papers  (2021-12-16T03:49:58Z)
• Detecting Invisible People [58.49425715635312]
  We re-purpose tracking benchmarks and propose new metrics for the task of detecting invisible objects. We demonstrate that current detection and tracking systems perform dramatically worse on this task. Second, we build dynamic models that explicitly reason in 3D, making use of observations produced by state-of-the-art monocular depth estimation networks.
  arXiv  Detail & Related papers  (2020-12-15T16:54:45Z)
• STCNet: Spatio-Temporal Cross Network for Industrial Smoke Detection [52.648906951532155]
  We propose a novel Spatio-Temporal Cross Network (STCNet) to recognize industrial smoke emissions. The proposed STCNet involves a spatial to extract texture features and a temporal pathway to capture smoke motion information. We show that our STCNet achieves clear improvements on the challenging RISE industrial smoke detection dataset against the best competitors by 6.2%.
  arXiv  Detail & Related papers  (2020-11-10T02:28:47Z)

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.