Can Deep Learning Trigger Alerts from Mobile-Captured Images?

• URL: http://arxiv.org/abs/2501.03499v1
• Date: Tue, 07 Jan 2025 03:39:43 GMT
• Title: Can Deep Learning Trigger Alerts from Mobile-Captured Images?
• Authors: Pritisha Sarkar, Duranta Durbaar Vishal Saha, Mousumi Saha,
• Abstract summary: This research contributes to verification of data augmentation techniques, CNN-based regression modelling for air quality prediction, and user-centric air quality monitoring through mobile technology. The proposed system offers practical solutions for individuals to make informed environmental health and well-being decisions.
• Score: 0.0594961162060159
• License:
• Abstract: Our research presents a comprehensive approach to leveraging mobile camera image data for real-time air quality assessment and recommendation. We develop a regression-based Convolutional Neural Network model and tailor it explicitly for air quality prediction by exploiting the inherent relationship between output parameters. As a result, the Mean Squared Error of 0.0077 and 0.0112 obtained for 2 and 5 pollutants respectively outperforms existing models. Furthermore, we aim to verify the common practice of augmenting the original dataset with a view to introducing more variation in the training phase. It is one of our most significant contributions that our experimental results demonstrate minimal accuracy differences between the original and augmented datasets. Finally, a real-time, user-friendly dashboard is implemented which dynamically displays the Air Quality Index and pollutant values derived from captured mobile camera images. Users' health conditions are considered to recommend whether a location is suitable based on current air quality metrics. Overall, this research contributes to verification of data augmentation techniques, CNN-based regression modelling for air quality prediction, and user-centric air quality monitoring through mobile technology. The proposed system offers practical solutions for individuals to make informed environmental health and well-being decisions.

Related papers

• Underwater Image Quality Assessment: A Perceptual Framework Guided by Physical Imaging [52.860312888450096]
  We propose a physically imaging-guided framework for underwater image quality assessment (UIQA) called PIGUIQA. We incorporate advanced physics-based underwater imaging estimation into our method and define distortion metrics that measure the impact of direct transmission attenuation and backwards scattering on image quality. PIGUIQA achieves state-of-the-art performance in underwater image quality prediction and exhibits strong generalizability.
  arXiv  Detail & Related papers  (2024-12-20T03:31:45Z)
• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models. In this paper, we investigate how detection performance varies across model backbones, types, and datasets. We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• Developing Gridded Emission Inventory from High-Resolution Satellite Object Detection for Improved Air Quality Forecasts [1.4238093681454425]
  This study presents an innovative approach to creating a dynamic, AI based emission inventory system for use with the Weather Research and Forecasting model coupled with Chemistry (WRF Chem) The system offers unprecedented temporal and spatial resolution in emission estimates, facilitating more accurate short term air quality forecasts and deeper insights into urban emission dynamics. Future work will focus on expanding the system's capabilities to non vehicular sources and further improving detection accuracy in challenging environmental conditions.
  arXiv  Detail & Related papers  (2024-10-14T01:32:45Z)
• Opinion-Unaware Blind Image Quality Assessment using Multi-Scale Deep Feature Statistics [54.08757792080732]
  We propose integrating deep features from pre-trained visual models with a statistical analysis model to achieve opinion-unaware BIQA (OU-BIQA) Our proposed model exhibits superior consistency with human visual perception compared to state-of-the-art BIQA models.
  arXiv  Detail & Related papers  (2024-05-29T06:09:34Z)
• DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception [78.26734070960886]
  Current perceptive models heavily depend on resource-intensive datasets. We introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability. Our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation.
  arXiv  Detail & Related papers  (2024-03-20T04:58:03Z)
• Air Quality Forecasting Using Machine Learning: A Global perspective with Relevance to Low-Resource Settings [0.0]
  Air pollution stands as the fourth leading cause of death globally. This study proposes a novel machine learning approach for accurate air quality prediction using two months of air quality data.
  arXiv  Detail & Related papers  (2024-01-09T05:52:02Z)
• Beyond Tides and Time: Machine Learning Triumph in Water Quality [0.0]
  This study aims to establish a robust predictive pipeline to both data science experts and those without domain specific knowledge. Our research aims to establish a robust predictive pipeline to both data science experts and those without domain specific knowledge.
  arXiv  Detail & Related papers  (2023-09-29T03:33:53Z)
• GreenEyes: An Air Quality Evaluating Model based on WaveNet [11.513011576336744]
  We propose a deep neural network model, which consists of a WaveNet-based backbone block for learning representations of sequences and an LSTM with a Temporal Attention module. We show our model can effectively predict the air quality level of the next timestamp given any segment of the air quality data from the data set.
  arXiv  Detail & Related papers  (2022-12-08T10:28:57Z)
• Negative Data Augmentation [127.28042046152954]
  We show that negative data augmentation samples provide information on the support of the data distribution. We introduce a new GAN training objective where we use NDA as an additional source of synthetic data for the discriminator. Empirically, models trained with our method achieve improved conditional/unconditional image generation along with improved anomaly detection capabilities.
  arXiv  Detail & Related papers  (2021-02-09T20:28:35Z)
• Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms [53.38353133198842]
  Air quality significantly affects human health, it is increasingly important to accurately and timely predict the Air Quality Index (AQI) This paper proposes a new federated learning-based aerial-ground air quality sensing framework for fine-grained 3D air quality monitoring and forecasting. For ground sensing systems, we propose a Graph Convolutional neural network-based Long Short-Term Memory (GC-LSTM) model to achieve accurate, real-time and future AQI inference.
  arXiv  Detail & Related papers  (2020-07-23T13:32:47Z)

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.