Detecting Wildfires on UAVs with Real-time Segmentation Trained by Larger Teacher Models

• URL: http://arxiv.org/abs/2408.10843v2
• Date: Thu, 12 Sep 2024 07:39:49 GMT
• Title: Detecting Wildfires on UAVs with Real-time Segmentation Trained by Larger Teacher Models
• Authors: Julius Pesonen, Teemu Hakala, Väinö Karjalainen, Niko Koivumäki, Lauri Markelin, Anna-Maria Raita-Hakola, Juha Suomalainen, Ilkka Pölönen, Eija Honkavaara,
• Abstract summary: Early detection of wildfires is essential to prevent large-scale fires resulting in extensive environmental, structural, and societal damage. Uncrewed aerial vehicles (UAVs) can cover large remote areas effectively with quick deployment requiring minimal infrastructure. In remote areas, however, the UAVs are limited to on-board computing for detection due to the lack of high-bandwidth mobile networks. This study shows how small specialised segmentation models can be trained using only bounding box labels.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Early detection of wildfires is essential to prevent large-scale fires resulting in extensive environmental, structural, and societal damage. Uncrewed aerial vehicles (UAVs) can cover large remote areas effectively with quick deployment requiring minimal infrastructure and equipping them with small cameras and computers enables autonomous real-time detection. In remote areas, however, the UAVs are limited to on-board computing for detection due to the lack of high-bandwidth mobile networks. This limits the detection to methods which are light enough for the on-board computer alone. For accurate camera-based localisation, segmentation of the detected smoke is essential but training data for deep learning-based wildfire smoke segmentation is limited. This study shows how small specialised segmentation models can be trained using only bounding box labels, leveraging zero-shot foundation model supervision. The method offers the advantages of needing only fairly easily obtainable bounding box labels and requiring training solely for the smaller student network. The proposed method achieved 63.3% mIoU on a manually annotated and diverse wildfire dataset. The used model can perform in real-time at ~25 fps with a UAV-carried NVIDIA Jetson Orin NX computer while reliably recognising smoke, demonstrated at real-world forest burning events. Code is available at https://gitlab.com/fgi_nls/public/wildfire-real-time-segmentation

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)
• Wildfire Detection Via Transfer Learning: A Survey [2.766371147936368]
  This paper surveys different publicly available neural network models used for detecting wildfires using regular visible-range cameras which are placed on hilltops or forest lookout towers. The neural network models are pre-trained on ImageNet-1K and fine-tuned on a custom wildfire dataset.
  arXiv  Detail & Related papers  (2023-06-21T13:57:04Z)
• Generalized Few-Shot 3D Object Detection of LiDAR Point Cloud for Autonomous Driving [91.39625612027386]
  We propose a novel task, called generalized few-shot 3D object detection, where we have a large amount of training data for common (base) objects, but only a few data for rare (novel) classes. Specifically, we analyze in-depth differences between images and point clouds, and then present a practical principle for the few-shot setting in the 3D LiDAR dataset. To solve this task, we propose an incremental fine-tuning method to extend existing 3D detection models to recognize both common and rare objects.
  arXiv  Detail & Related papers  (2023-02-08T07:11:36Z)
• 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 Fire Detection in Industrial Environments with YOLOv4 [53.180678723280145]
  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.
  arXiv  Detail & Related papers  (2022-12-09T11:32:36Z)
• Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images [96.66271207089096]
  FCOS-LiDAR is a fully convolutional one-stage 3D object detector for LiDAR point clouds of autonomous driving scenes. We show that an RV-based 3D detector with standard 2D convolutions alone can achieve comparable performance to state-of-the-art BEV-based detectors.
  arXiv  Detail & Related papers  (2022-05-27T05:42:16Z)
• Analyzing Multispectral Satellite Imagery of South American Wildfires Using CNNs and Unsupervised Learning [0.0]
  This study trains a Fully Convolutional Neural Network with skip connections on Landsat 8 images of Ecuador and the Galapagos. Image segmentation is conducted on the Cirrus Cloud band using K-Means Clustering to simplify continuous pixel values into three discrete classes. Two additional Convolutional Neural Networks are trained to classify the presence of a wildfire in a patch of land.
  arXiv  Detail & Related papers  (2022-01-19T02:45:01Z)
• 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)
• DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows [52.31831255787147]
  We introduce a novel technique, DAAIN, to detect out-of-distribution (OOD) inputs and adversarial attacks (AA) Our approach monitors the inner workings of a neural network and learns a density estimator of the activation distribution. Our model can be trained on a single GPU making it compute efficient and deployable without requiring specialized accelerators.
  arXiv  Detail & Related papers  (2021-05-30T22:07:13Z)
• Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication [53.46235596543596]
  We study the problem of air-to-ground ultra-reliable and low-latency communication (URLLC) for a moving ground user. We propose a novel multi-agent deep reinforcement learning framework, coined a graph attention exchange network (GAXNet) GAXNet achieves 6.5x lower latency with the target 0.0000001 error rate, compared to a state-of-the-art baseline framework.
  arXiv  Detail & Related papers  (2021-05-22T12:43:25Z)
• Cooling-Shrinking Attack: Blinding the Tracker with Imperceptible Noises [87.53808756910452]
  A cooling-shrinking attack method is proposed to deceive state-of-the-art SiameseRPN-based trackers. Our method has good transferability and is able to deceive other top-performance trackers such as DaSiamRPN, DaSiamRPN-UpdateNet, and DiMP.
  arXiv  Detail & Related papers  (2020-03-21T07:13:40Z)

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.