Liquid Leak Detection Using Thermal Images

• URL: http://arxiv.org/abs/2312.10980v1
• Date: Mon, 18 Dec 2023 07:03:35 GMT
• Title: Liquid Leak Detection Using Thermal Images
• Authors: Kalpak Bansod, Yanshan Wan, and Yugesh Rai
• Abstract summary: This paper presents a comprehensive solution to address the critical challenge of liquid leaks in the oil and gas industry. Our project focuses on enhancing early identification of liquid leaks in key infrastructure components such as pipelines, pumps, and tanks. Through the integration of surveillance thermal cameras and sensors, the combined YOLO and RT DETR models demonstrate remarkable efficacy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a comprehensive solution to address the critical challenge of liquid leaks in the oil and gas industry, leveraging advanced computer vision and deep learning methodologies. Employing You Only Look Once (YOLO) and Real-Time Detection Transformer (RT DETR) models, our project focuses on enhancing early identification of liquid leaks in key infrastructure components such as pipelines, pumps, and tanks. Through the integration of surveillance thermal cameras and sensors, the combined YOLO and RT DETR models demonstrate remarkable efficacy in the continuous monitoring and analysis of visual data within oil and gas facilities. YOLO's real-time object detection capabilities swiftly recognize leaks and their patterns, while RT DETR excels in discerning specific leak-related features, particularly in thermal images. This approach significantly improves the accuracy and speed of leak detection, ultimately mitigating environmental and financial risks associated with liquid leaks.

Related papers

• Drone Detection with Event Cameras [8.679862302950614]
  Event cameras virtually eliminate motion blur and enable consistent detection in extreme lighting.<n>This work demonstrates that event-based vision provides a powerful foundation for the next generation of reliable, low-latency, and efficient counter-UAV systems.
  arXiv  Detail & Related papers  (2025-08-06T15:49:33Z)
• YOLO-Based Pipeline Monitoring in Challenging Visual Environments [0.0]
  Condition monitoring subsea pipelines in low-visibility underwater environments poses significant challenges due to turbidity, light distortion, and image degradation.<n>Traditional visual-based inspection systems often fail to provide reliable data for mapping, object recognition, or defect detection in such conditions.<n>This study explores the integration of advanced artificial intelligence (AI) techniques to enhance image quality, detect pipeline structures, and support autonomous fault diagnosis.
  arXiv  Detail & Related papers  (2025-06-30T14:47:30Z)
• Real-Time Aerial Fire Detection on Resource-Constrained Devices Using Knowledge Distillation [29.875425833515973]
  Wildfire catastrophes cause significant environmental degradation, human losses, and financial damage. Current systems rely primarily on fixed CCTV cameras with a limited field of view, restricting their effectiveness in large outdoor environments. We present a lightweight fire detection model based on MobileViT-S, compressed through the distillation of knowledge from a stronger teacher model.
  arXiv  Detail & Related papers  (2025-02-28T11:49:58Z)
• Towards an Autonomous Surface Vehicle Prototype for Artificial Intelligence Applications of Water Quality Monitoring [68.41400824104953]
  This paper presents a vehicle prototype that addresses the use of Artificial Intelligence algorithms and enhanced sensing techniques for water quality monitoring. The vehicle is fully equipped with high-quality sensors to measure water quality parameters and water depth. By means of a stereo-camera, it also can detect and locate macro-plastics in real environments.
  arXiv  Detail & Related papers  (2024-10-08T10:35:32Z)
• Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future [119.88454942558485]
  Underwater object detection (UOD) aims to identify and localise objects in underwater images or videos. In recent years, artificial intelligence (AI) based methods, especially deep learning methods, have shown promising performance in UOD.
  arXiv  Detail & Related papers  (2024-10-08T00:25:33Z)
• Binocular Model: A deep learning solution for online melt pool temperature analysis using dual-wavelength Imaging Pyrometry [0.0]
  In metal Additive Manufacturing (AM), monitoring the temperature of the Melt Pool (MP) is crucial for ensuring part quality, process stability, defect prevention, and overall process optimization. Traditional methods, are slow to converge and require extensive manual effort to translate data into actionable insights. We propose an Artificial Intelligence (AI)-based solution aimed at reducing manual data processing reliance.
  arXiv  Detail & Related papers  (2024-08-20T18:26:09Z)
• Research on Detection of Floating Objects in River and Lake Based on AI Intelligent Image Recognition [12.315852697312195]
  This study focuses on the detection of floating objects in river and lake environments, exploring an innovative approach based on deep learning. The proposed system has demonstrated its ability to significantly enhance the accuracy and efficiency of debris detection, thus offering a new technological avenue for water quality monitoring in rivers and lakes.
  arXiv  Detail & Related papers  (2024-04-10T10:13:37Z)
• Knowledge Distillation in YOLOX-ViT for Side-Scan Sonar Object Detection [0.40498500266986387]
  We present YOLOX-ViT, a novel object detection model, and investigate the efficacy of knowledge distillation for model size reduction. We introduce a new side-scan sonar image dataset, and use it to evaluate our object detector's performance.
  arXiv  Detail & Related papers  (2024-03-14T12:03:28Z)
• D-YOLO a robust framework for object detection in adverse weather conditions [0.0]
  Adverse weather conditions including haze, snow and rain lead to decline in image qualities, which often causes a decline in performance for deep-learning based detection networks. To better integrate image restoration and object detection tasks, we designed a double-route network with an attention feature fusion module. We also proposed a subnetwork to provide haze-free features to the detection network. Specifically, our D-YOLO improves the performance of the detection network by minimizing the distance between the clear feature extraction subnetwork and detection network.
  arXiv  Detail & Related papers  (2024-03-14T09:57:15Z)
• Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
  Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems. This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
  arXiv  Detail & Related papers  (2023-08-14T16:48:57Z)
• A Smart Robotic System for Industrial Plant Supervision [16.68349850187503]
  We present a system consisting of an autonomously navigating robot integrated with various sensors and intelligent data processing. It is able to detect methane leaks and estimate its flow rate, detect more general gas anomalies, localize sound sources and detect failure cases, map the environment in 3D, and navigate autonomously.
  arXiv  Detail & Related papers  (2023-08-10T14:54:21Z)
• 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)
• 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)
• Energy Drain of the Object Detection Processing Pipeline for Mobile Devices: Analysis and Implications [77.00418462388525]
  This paper presents the first detailed experimental study of a mobile augmented reality (AR) client's energy consumption and the detection latency of executing Convolutional Neural Networks (CNN) based object detection. Our detailed measurements refine the energy analysis of mobile AR clients and reveal several interesting perspectives regarding the energy consumption of executing CNN-based object detection.
  arXiv  Detail & Related papers  (2020-11-26T00:32:07Z)

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.