SYSTEM AND METHOD FOR HUMAN DETECTION IN THERMAL IMAGING TECHNOLOGY

Abstract

The present invention discloses a novel system and method for human detection utilizing advanced thermal imaging technology. By capturing infrared radiation and converting it into thermal images, the system enhances the identification and localization of individuals, making it crucial for applications such as search and rescue, surveillance, and security, especially under low visibility conditions. It employs sophisticated algorithms, including machine learning and deep learning models like Convolutional Neural Networks (CNNs) and YOLO-NAS, to significantly improve detection accuracy and reduce false positives. The invention incorporates image preprocessing, noise reduction techniques, and real-time surveillance capabilities, ensuring reliable performance in diverse operational contexts. Additionally, it features an advanced user interface for customizable detection parameters and emphasizes non-intrusive operation to respect privacy. Overall, this invention represents a significant advancement in thermal imaging and human detection, addressing the limitations of existing technologies and enhancing safety and efficiency in various environments. Accompanied Drawing [Figure 1-8]

Specification

Description:[001] The present invention relates to the field of thermal imaging technology, specifically focusing on systems and methods for human detection within thermal images. By exploring and comparing the capabilities of human observers and machine vision algorithms, this invention aims to contribute to advancements in the performance, efficiency, and accuracy of human detection processes using thermal imagery.
BACKGROUND OF THE INVENTION
[002] Thermal imaging technology has emerged as a crucial tool for identifying and locating individuals, particularly in adverse environments characterized by poor visibility due to weather conditions, darkness, or smoke. This technology is invaluable for applications such as search and rescue operations, surveillance, and security, where timely and accurate detection can significantly impact outcomes. Given the critical nature of these applications, advancements in human detection capabilities using thermal imagery are essential for enhancing operational efficiency and safety.
[003] Numerous studies have explored the capabilities of thermal imaging for human detection, contributing to a growing body of research aimed at leveraging this technology for various applications. For instance, Marina Ivašic-Kos et al. (2019) investigated human detection using the YOLO framework in thermal images, demonstrating the potential of deep learning algorithms in improving detection accuracy.
[004] Similarly, Mate Kristo et al. (2020) retrained object detection models on thermal image datasets captured in diverse weather conditions, underlining the importance of model adaptability. Other researchers, including More Rahul Tanaji et al. (2021) and X. Wang et al. (2019), have examined the intricacies of human detection, exploring factors like human posture and motion information to enhance detection confidence and accuracy.
[005] Despite these advancements, existing prior arts exhibit several shortcomings that hinder their practical application. A significant drawback is the reliance on specific environmental conditions, as many existing algorithms struggle with variable weather and lighting, leading to reduced accuracy and higher false positive rates.
[006] For example, while approaches like the YOLO model have demonstrated effectiveness in controlled conditions, they often require extensive fine-tuning to operate effectively in real-world scenarios marked by environmental variability. Additionally, many prior art solutions focus primarily on detecting human forms rather than integrating robust algorithms capable of distinguishing humans from other heat-emitting objects, such as animals or machinery, which complicates detection efforts.
[007] The present invention overcomes these limitations by introducing a comprehensive system and method for human detection in thermal imagery that combines advanced machine learning techniques with real-time monitoring capabilities. Unlike previous systems that rely heavily on visible light or specific sensor technologies, this invention leverages the unique advantages of thermal imaging to ensure high accuracy even in complete darkness or adverse weather. The integration of sophisticated algorithms tailored for human detection significantly enhances the precision of identifying individuals, reducing false positives and providing robust operational performance.
[008] Moreover, the invention enables real-time surveillance, ensuring immediate alerts for security and search and rescue operations. Its adaptability across various sectors-including security, industrial safety, and emergency response-positions it as a versatile solution, addressing the multifaceted challenges of human detection. By minimizing false alarms and ensuring non-intrusive operation, this invention promises to enhance safety and operational efficiency in critical situations. Ultimately, the technology presented in this patent not only advances the field of thermal imaging but also contributes to safer and more effective human detection methodologies in various applications.
SUMMARY OF THE PRESENT INVENTION
[009] The present invention relates to a System and Method for Human Detection in Thermal Imaging Technology, addressing the need for effective human recognition in challenging conditions where traditional visual cues are insufficient. This system leverages advanced thermal imaging techniques to detect and identify individuals based on their unique heat signatures, enabling accurate surveillance, search, and rescue operations, even in low-light or adverse weather situations. By incorporating sophisticated algorithms, including deep learning models such as YOLO-NAS, the invention significantly enhances detection accuracy while minimizing false positives, thereby ensuring reliable performance in diverse environments. This approach distinguishes itself from conventional systems by focusing on thermal emissions, enabling precise and non-intrusive human detection without the need for visible light or active sensors.
[010] The methodology of this invention encompasses rigorous comparative studies between human observers and automated machine vision systems, elucidating their strengths and limitations in thermal image analysis. The system is designed for real-time monitoring, providing immediate alerts in security scenarios and emergency situations. Furthermore, its adaptability allows deployment across various applications, including industrial safety, healthcare, and wildlife conservation. The invention's robust architecture ensures compatibility with edge computing devices, facilitating its integration into existing infrastructures. By utilizing advanced infrared sensor technology and sophisticated noise reduction techniques, this invention not only optimizes detection capabilities but also contributes to sustainability efforts by reducing reliance on additional lighting. Overall, this project aims to revolutionize human detection in thermal imaging, enhancing situational awareness and safety in critical operational contexts.
[011] In this respect, before explaining at least one object of the invention in detail, it is to be understood that the invention is not limited in its application to the details of set of rules and to the arrangements of the various models set forth in the following description or illustrated in the drawings. The invention is capable of other objects and of being practiced and carried out in various ways, according to the need of that industry. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[012] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[013] When considering the following thorough explanation of the present invention, it will be easier to understand it and other objects than those mentioned above will become evident. Such description refers to the illustrations in the annex, wherein:
Figure 1 illustrates pre-Trained systems associated with the present invention;
Figure 2 illustrates comparative Analysis of various system;
Figure 3 illustrates block Diagram of proposed system;
Figure 4 illustrates level 0 data flow diagram (DFD);
Figure 5 illustrates level 1 data flow diagram (DFD);
Figure 6 illustrates level 2 data flow diagram (DFD);
Figure 7 illustrates overall Architecture Diagram associated with the proposed system; and
Figure 8 illustrates use Case Diagram associated with the proposed system, in accordance with an embodiment of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[014] The following sections of this article will provided various embodiments of the current invention with references to the accompanying drawings, whereby the reference numbers utilised in the picture correspond to like elements throughout the description. However, this invention is not limited to the embodiment described here and may be embodied in several other ways. Instead, the embodiment is included to ensure that this disclosure is extensive and complete and that individuals of ordinary skill in the art are properly informed of the extent of the invention.
[015] Numerical values and ranges are given for many parts of the implementations discussed in the following thorough discussion. These numbers and ranges are merely to be used as examples and are not meant to restrict the claims' applicability. A variety of materials are also recognised as fitting for certain aspects of the implementations. These materials should only be used as examples and are not meant to restrict the application of the innovation.
[016] Referring to Figure 1-8, the present invention relates to a novel system and method for human detection utilizing advanced thermal imaging technology. This invention aimed at enhancing the ability to identify and locate individuals in thermal images, which is critical for various applications such as search and rescue operations, surveillance, and security, especially under conditions where traditional visual cues are limited. By systematically examining and comparing human vision and machine vision systems, this project seeks to uncover their respective strengths and weaknesses in recognizing individuals in thermal imagery.
[017] Thermal imaging technology is based on the capture of infrared radiation emitted by objects, converting this radiation into thermal images that represent the heat signatures of individuals. Unlike conventional methods that rely on visible light, thermal imaging excels in low-light and adverse weather conditions, ensuring accuracy even in complete darkness. The ability to detect human heat emissions provides a reliable means of identifying individuals in environments where visibility is compromised, such as during search and rescue missions in smoke-filled areas or during nighttime operations.
[018] Central to the invention is the development of sophisticated algorithms and machine learning techniques specifically tailored for human detection within thermal images. By employing advanced deep learning models, including Convolutional Neural Networks (CNNs) and the state-of-the-art YOLO-NAS (You Only Look Once - Neural Architecture Search), the system demonstrates a marked enhancement in accuracy, significantly reducing instances of false positives and improving the ability to differentiate humans from other heat-emitting entities. YOLO-NAS, developed by Deci AI, represents a major leap in object detection technology, offering improved quantization support and accuracy-latency trade-offs. The model's architecture, AutoNAC, provides an optimal balance of accuracy and processing speed, which is crucial for real-time applications.
[019] The invention employs various image preprocessing techniques to optimize performance. Image resizing is a key preprocessing step that standardizes input dimensions, improving computational efficiency and facilitating the detection of objects at different scales without distorting their aspect ratios. Additionally, feature extraction is conducted using deep CNNs, which capture increasingly abstract visual patterns through a hierarchy of convolutional and subsampling layers. This enables the identification and localization of human figures within the thermal imagery.
[020] A crucial component of this invention is the integration of noise reduction techniques that enhance the reliability of object detection. Traditional detection systems often struggle with false alarms caused by environmental factors. This invention employs methods such as Non-Maximum Suppression (NMS), which eliminates overlapping bounding boxes to retain only those with the highest confidence scores.
[021] Furthermore, a confidence score threshold is set to filter out low-confidence detections, thereby enhancing overall detection accuracy. The use of high-quality training data and post-processing filters further aids in minimizing noise and improving detection reliability.
[022] The architecture of the system is designed for real-time surveillance capabilities, enabling continuous monitoring of thermal imagery and immediate alerts for critical situations. This characteristic is essential for applications demanding swift reactions, such as security in public spaces and search and rescue operations. By employing state-of-the-art infrared sensor technology, the system ensures precise heat detection even in challenging lighting conditions.
[023] The invention's versatility is demonstrated by its applicability across various domains, including security, industrial safety, healthcare, and wildlife conservation. Its adaptability allows it to function effectively in diverse operational contexts, which distinguishes it from single-purpose detection systems. For instance, in the realm of industrial safety, the system can monitor hazardous areas for unauthorized personnel, while in healthcare, it can assist in detecting individuals in need of urgent medical attention during emergencies.
[024] Moreover, the invention emphasizes a non-intrusive operation, allowing for human detection without necessitating direct exposure to light or the emission of signals. This feature ensures the privacy and comfort of individuals being monitored, addressing ethical concerns associated with surveillance technologies. Additionally, the system is designed for compatibility with various edge computing devices and platforms, enhancing its versatility and applicability in real-world scenarios.
[025] To validate the effectiveness of the invention, rigorous empirical studies have been conducted comparing the performance of human observers and machine algorithms in recognizing individuals within thermal images. The results of these studies indicate that while human observers excel in qualitative elucidation of complex, unorganized scenes, machine algorithms significantly outperform humans in quantitative computations, particularly in structured environments. This finding underscores the complementary strengths of human and machine vision systems, paving the way for future collaborations in enhancing detection accuracy.
[026] The invention also incorporates an advanced user interface with interactive features that allow users to configure and customize detection parameters according to specific operational requirements. This user-friendly interface ensures that the technology can be adapted to various applications without requiring extensive technical expertise, thereby broadening its usability.
[027] Furthermore, the integration of advanced machine learning techniques within the system allows for continuous improvement in detection capabilities. By leveraging large datasets and automatic labeling, the model learns from its predictions, refining its accuracy over time. This self-improving capability positions the invention as a pioneering solution in the field of thermal imaging and human detection.
[028] In conclusion, the present invention establishes a comprehensive and multifaceted approach to human detection in thermal imaging, offering several advantages over conventional technologies. With its precision in thermal imaging, elevated accuracy, real-time surveillance capabilities, and versatility across diverse applications, this invention represents a significant advancement in the field.
[029] By incorporating advanced algorithms, sophisticated noise reduction techniques, and user-friendly interfaces, it addresses the limitations of existing systems while enhancing safety, efficiency, and situational awareness in various operational contexts. The project's emphasis on empirical validation and continuous improvement ensures that it remains at the forefront of technological advancements in human detection within thermal imaging systems.
[030] It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-discussed embodiments may be used in combination with each other. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description.
[031] The benefits and advantages which may be provided by the present invention have been described above with regard to specific embodiments. These benefits and advantages, and any elements or limitations that may cause them to occur or to become more pronounced are not to be construed as critical, required, or essential features of any or all of the embodiments.
, Claims:1. A system for human detection in thermal imaging technology, comprising:
a) a thermal imaging sensor configured to capture infrared radiation emitted by objects;
b) a processing unit configured to convert the captured infrared radiation into thermal images;
c) advanced algorithms, including machine learning techniques and deep learning models, for identifying and locating individuals within the thermal images;
d) a user interface that allows users to configure detection parameters.
2. A method for human detection in thermal imaging technology, comprising the steps of:
i. capturing infrared radiation using a thermal imaging sensor;
ii. converting the infrared radiation into thermal images;
iii. applying advanced algorithms and deep learning models to identify human figures within the thermal images;
iv. generating alerts based on the detection of individuals;
wherein the deep learning models include Convolutional Neural Networks (CNNs) and YOLO-NAS, optimized for detecting human heat emissions and minimizing false positives.
3. The system as claimed in claim 1, further includes image preprocessing techniques, including image resizing and feature extraction, to enhance detection performance.
4. The system as claimed in claim 1, wherein noise reduction techniques, including Non-Maximum Suppression (NMS) and confidence score thresholds, are employed to improve detection accuracy.
5. The method as claimed in claim 2, further including the step of utilizing high-quality training data and post-processing filters to minimize noise and improve detection reliability.
6. The system as claimed in claim 1, wherein the architecture of the system is designed for real-time surveillance capabilities, enabling continuous monitoring of thermal imagery.
7. The system as claimed in claim 1, wherein the user interface includes interactive features that allow for customization of detection parameters without extensive technical expertise.
8. The method as claimed in claim 2, wherein the continuous improvement in detection capabilities is achieved by leveraging large datasets and automatic labeling for training the models.
9. The system as claimed in claim 1, further characterized by its non-intrusive operation, allowing for human detection without direct exposure to light or the emission of signals, thereby addressing ethical concerns associated with surveillance technologies.

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