Integrated AI Surveillance Ecosystem: Enhancing Industrial Health and Safety through a Synergistic A

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 10 Issue: 11 | Nov 2023

p-ISSN: 2395-0072

www.irjet.net

Integrated AI Surveillance Ecosystem: Enhancing Industrial Health and Safety through a Synergistic Approach of Drones and CCTV Ms. Vandana Tripathi1, Mr. Lalit Shukla2, Dr. Sandeep Mathur3 1Assistant Professor, Business Management Department, Noida International University, Noida, Uttar Pradesh, 2Computer Vision Engineer, GarudaUAV Soft Solutions Pvt. Ltd. , Noida, Uttar Pradesh

3Professor, School of Science Department, Noida International University, Noida, Uttar Pradesh ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract - The efficacy of industrial surveillance is ● Increased Accuracy: AI algorithms excel in

hindered by the limitations of human-centric monitoring, such as fatigue, limited scope, and delayed responses. This study introduces an innovative AI-powered surveillance ecosystem that integrates drones with CCTV, harnessing AI's real-time analysis to enhance the detection of safety and security violations in vast industrial areas[1]. The utilization of tethered and autonomous drones expands the coverage, enabling access to previously unreachable zones. This integrated approach offers a holistic view of industrial environments, from traffic management to PPE adherence, ensuring a higher standard of monitoring.

processing and analyzing vast amounts of data, surpassing human capabilities in detecting anomalies and potential threats. This heightened accuracy is crucial for ensuring industrial safety and preventing accidents.

● Cost Efficiency: By automating key surveillance tasks like video feed monitoring and alert generation, AI systems significantly reduce the operational costs associated with manual surveillance.

Key Words: Industrial surveillance, AI-driven ecosystem, Drones, CCTV, Safety breaches detection, Real-time analytics.

● Enhanced Safety Measures: AI surveillance contributes to safer industrial environments by proactively identifying hazards and potential accidents, thus enabling preemptive action to avert disasters.

1. INTRODUCTION Ensuring health and safety within industrial environments is crucial, given the elevated risks posed by complex machinery, hazardous chemicals, and sophisticated operational procedures. This paper proposes a novel, integrated AI surveillance ecosystem that marries the versatility of drones with the reliability of CCTV systems to overcome the inherent limitations of human monitoring in industrial settings. The proposed model leverages AI to analyze surveillance data in real-time, facilitating prompt and precise identification of safety and security infractions. By addressing these objectives, the study seeks to contribute to the burgeoning field of AI in industrial safety, offering tangible solutions for persistent challenges in surveillance and hazard detection[2].

2.1 Drone and CCTV Integration in Surveillance: The synergy between drones and CCTV systems represents a notable innovation in AI-based surveillance.

Drones, in particular, offer several unique advantages:

● Mobility: Their ability to access remote and

otherwise inaccessible locations provides surveillance capabilities far beyond the reach of stationary CCTV systems.

● Versatility: Equipped with a range of sensors, including standard and thermal imaging cameras, drones can perform a wide array of surveillance tasks, making them indispensable tools for modern surveillance needs.

2. Literature Review Artificial Intelligence in Industrial Surveillance:

● Scalability: Drones offer the flexibility to scale surveillance operations according to situational demands, a feature particularly useful in dynamic industrial environments.

The industrial landscape is undergoing a transformative phase with the advent of Artificial Intelligence (AI).

Surveillance, a critical aspect of industrial safety, has particularly benefited from AI integration[3]. AI-powered surveillance systems offer a multitude of advantages over traditional human-centered monitoring:

© 2023, IRJET

|

Impact Factor value: 8.226

2.2 Challenges and Opportunities for Improvement: Despite the advancements, AI-powered surveillance systems are not without challenges. Key areas of improvement include:

|

ISO 9001:2008 Certified Journal

|

Page 724

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 10 Issue: 11 | Nov 2023

p-ISSN: 2395-0072

www.irjet.net

instantaneously, providing a comprehensive view of the entire industrial site.

● Vulnerability to Adversarial Attacks: AI

systems, by their nature, can be susceptible to sophisticated cyber-attacks aimed at compromising their functionality or accuracy.

● Responsive Alert System: Beyond data collection,

the system should be adept at generating timely alerts and initiating appropriate corrective actions in response to detected threats or anomalies.

● False Positives: The propensity of AI systems to

generate false alerts can lead to operational inefficiencies, necessitating further refinement of AI algorithms to enhance their discriminative capabilities.

3. Health and Safety Surveillance Ecosystem 3.1 AI-Driven Surveillance in Industrial Safety: AI-powered surveillance systems are highly versatile, with applications extending across various aspects of industrial health and safety:

● PPE

Compliance Monitoring: Automated systems for ensuring worker adherence to Personal Protective Equipment (PPE) standards, significantly reducing the risk of workplace injuries.

Fig -1: Image showing the surveillance ecosystem in industrial areas, generated using DALL-E. 3.3 Proposed Methods and Approaches

● Unauthorized Access Detection: Capable of identifying and alerting security personnel about unauthorized entries into restricted or hazardous areas within industrial premises.

Creating a Comprehensive Surveillance Ecosystem:

● Cloud-Based Platform: Centralizing data from drones, CCTVs, and sensors on a cloud platform facilitates access to AI algorithms for analysis and alert generation, creating an interconnected and intelligent surveillance network.

● Hazard Identification: Proactively detecting potential hazards, such as gas leaks or fire risks, thus enabling early intervention and prevention of accidents.

● Drone Fleet Integration: Drones play a pivotal

● Traffic and Congestion Management: AI

role in this ecosystem, covering areas beyond the reach of static CCTVs. Equipped with various sensors, they enhance the surveillance scope and depth.

surveillance helps in managing vehicular traffic within industrial sites, identifying potential congestion points, and contributing to safer and more efficient site navigation.

● Collaborative Data Processing and Control:

● Confined

Space Monitoring: Specialized surveillance of confined spaces, often challenging to monitor with traditional methods, ensuring safety compliance and detecting any unsafe activities.

The cloud platform enables seamless integration of drone data, allowing for coordinated deployment and real-time tracking, thus optimizing surveillance efficiency.

4. Proposed Ecosystem: Integrated Control and Command Centre with Drones and CCTV

3.2 Ecosystem for Industrial Surveillance Developing an Integrated Surveillance System:

4.1 Synergizing Drones and CCTVs for Enhanced Surveillance:

To maximize the benefits of AI in industrial surveillance, a comprehensive ecosystem integrating drones, CCTVs, and other sensory devices is essential:

This proposed model leverages the strengths of both drones and CCTVs to provide unprecedented surveillance capabilities:

● Real-Time Data Collection and Analysis: The

● Extended Reach and Coverage: Drones, both

ecosystem should have the capability to gather and analyze data from various sources

© 2023, IRJET

|

Impact Factor value: 8.226

tethered and mobile, complement the persistent

|

ISO 9001:2008 Certified Journal

|

Page 725

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 10 Issue: 11 | Nov 2023

p-ISSN: 2395-0072

www.irjet.net

monitoring of CCTVs by covering challenging terrains and inaccessible areas, ensuring no blind spots in surveillance.

● Equipment Monitoring: Ensuring operational safety by monitoring machinery for proper functioning and detecting potential malfunctions, thus averting accidents.

● Advanced AI Algorithms for Data Analysis:

● Hazardous Material Spillage Detection: Rapid identification and alerting in cases of accidental spillage or leaks of hazardous materials, facilitating prompt response and mitigation.

Incorporating state-of-the-art AI algorithms like YOLOv8 for object detection, and employing Neural Architecture Search (NAS) for model optimization, ensures the system's adaptability and precision in various industrial surveillance scenarios.

4.2 Integrating into the ICCC Centralized Response:

● Hardware Integration: The system architecture

includes a strategic mix of tethered and mobile drones, well-positioned CCTVs, edge servers for localized data processing, and LoRa gateways for robust communication, creating a cohesive surveillance network.

Command

for

Coordinated

Surveillance

The Integrated Command and Control Center (ICCC) acts as the operational hub, where all surveillance feeds, analytics, and alerts are unified for efficient management:

● Real-Time Alert Generation and Management:

Upon the detection of any anomalies or safety breaches, the ICCC generates immediate alerts, facilitating swift and appropriate responses.

● Coordinated Response Strategies: Depending on the nature of the alert, the system can deploy drones for detailed inspection or activate emergency protocols, ensuring an effective and timely response to incidents.

● Unified Dashboard for Enhanced Oversight:

Fig -2: Information Flow diagram of Ecosystem .

The ICCC provides a comprehensive view of the entire surveillance network, allowing for informed decisions and coordinated actions across the industrial site.

4. Use Cases and Application of Algorithms 4.1 Leveraging AI for Diverse Industrial Surveillance Scenarios: ● Vehicle Tracking: Employing advanced algorithms such as YOLOv8 for dynamic and precise tracking of vehicles within industrial premises, enhancing operational safety and logistics management. ● Confined Space Violation Detection: Utilizing drones, particularly tethered variants, to monitor confined spaces. Neural Architecture Search (NAS) is instrumental in adapting AI models to the unique structural complexities of these areas. ● PPE Violation Detection: Implementing segmentation and classification algorithms to monitor compliance with safety attire protocols, crucial for worker safety. ● Unauthorized Entry Detection: Continuously monitoring entry points with CCTVs and integrating AI to alert on unrecognized or unauthorized individuals, bolstering site security.

© 2023, IRJET

|

Impact Factor value: 8.226

Fig -3: Integrated Control and Command Centre for tracking and visualization of violations and alerts, Image generated using DALL-E.

|

ISO 9001:2008 Certified Journal

|

Page 726

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 10 Issue: 11 | Nov 2023

p-ISSN: 2395-0072

www.irjet.net

5. Results and Discussion Performance Evaluation of the AI-Driven Surveillance Ecosystem:

● Methodology: The system's effectiveness was

assessed through simulations, replicating a typical industrial environment. Its performance was compared against traditional human-monitored surveillance and standalone AI systems.

● Evaluation Criteria: The assessment focused on three key areas - accuracy of threat detection, speed of threat detection, and overall site coverage.

5.1 Results

● Accuracy of Threat Detection: The integrated AI system demonstrated a significantly higher accuracy (96.5%) in identifying potential hazards compared to traditional (78.3%) and standalone AI systems (88.4%).

Fig -4: Real-time Detection of anomalies PPE kit (Helmet, IFR suit, Gloves, Shoes) and Mask.

● Speed of Threat Detection: The AI-integrated

system detected threats in an average of 2.3 seconds, markedly faster than the traditional (15.7 seconds) and standalone AI systems (5.1 seconds).

3. CONCLUSIONS This study demonstrates the significant advancements an integrated AI-driven surveillance ecosystem can bring to industrial settings. By combining drones and CCTVs, we have shown a system that surpasses traditional and standalone AI surveillance methods in accuracy, speed, and coverage[4]. This approach not only enhances safety protocols but also introduces versatility in addressing a range of industrial safety concerns, from PPE compliance to hazard detection[5]. Despite its many advantages, the system also poses challenges related to privacy and data management, which necessitate ongoing refinement and ethical consideration. Looking forward, the implications of this technology extend far beyond immediate safety applications, promising to shape future industrial safety standards and practices. As we continue to explore and develop these AI-infused systems, their potential to revolutionize industrial safety and efficiency becomes increasingly clear[6].

● Overall Site Coverage: The proposed system achieved near-complete coverage (98.7%) of the industrial site, surpassing both traditional (84.1%) and standalone AI systems (91.2%).

5.2 Discussion The results highlight the integrated system's superior performance in ensuring extensive coverage and rapid, accurate threat detection. The system's adaptability suggests potential for further optimization in specific industrial contexts.

Table -1: Table showing the percentage efficiency in terms of Accuracy, Speed and Overall coverage in Proposed, Traditional and Standalone AI system Proposed AI System

Traditional System

Standalone AI System

Accuracy of Threat Detection (%)

96.5

78.3

88.4

Speed of Threat Detection (seconds)

2.3

15.7

5.1

Overall Site Coverage (%)

98.7

84.1

91.2

© 2023, IRJET

|

Criteria

Impact Factor value: 8.226

ACKNOWLEDGEMENT We express our heartfelt gratitude to all those who contributed to the success of this research. Special thanks to the technical and academic peers who provided invaluable insights and constructive critiques during the development of this work. We are also grateful to GarudaUAV, whose resources and support were instrumental in conducting this research. Our appreciation extends to the team members and colleagues for their unwavering support, collaboration, and dedication. Lastly,

|

ISO 9001:2008 Certified Journal

|

Page 727

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 10 Issue: 11 | Nov 2023

p-ISSN: 2395-0072

www.irjet.net

we thank our families and friends for their encouragement and understanding throughout the course of this study.

REFERENCES [1]

Kadam, Amol & Patil, Tulshihar & Abhang, Reshma.

(2023). View of Innovative AI-driven Automation System Leveraging Advanced Perceptive Technologies to Establish an Ideal Self-Regulating Video Surveillance Model. Tuijin Jishu/Journal of Propulsion Technology. 44. 592-603. 10.52783/tjjpt.v44.i2.220.

[2]

Fan, Zhencheng & Yan, Zheng & Wen, Shiping. (2023).

Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health. Sustainability. 15. 13493.

10.3390/su151813493. .

[3]

Clarke, Roger. (2022). Responsible application of artificial intelligence to surveillance: What prospects?.

Information Polity. 27. 1-17. 10.3233/IP-211532.

[4]

Gohari, Adel & Ahmad, Anuar & Rahim, Ruzairi & Supa'at, A.S.M. & Razak, Shukor & Gismalla, Mohammed. (2022). Involvement of Surveillance Drones in Smart Cities: A Systematic Review. IEEE Access. 1-1. 10.1109/ACCESS.2022.3177904.

[5]

Riaz, Mudassar & He, Jianbiao & Xie, Kai & Alsagri, Hatoon & Moqurrab, Syed & Alhakbani, Haya & Obidallah, Waeal. (2023). Enhancing Workplace Safety: PPE_Swin—A Robust Swin Transformer Approach for Automated Personal Protective Equipment Detection. Electronics. 12. 4675.

10.3390/electronics12224675.

[6]

Uduma, Igwe & Otamiri, Humble & Andrew, Imah.

(2023). IMPACT OF ARTIFICIAL INTELLIGENCE (AI) APPLICABILITY IN THE OPTIMIZATION OF MANUFACTURING PROCESSES IN SMEs WITHIN EBONYI STATE, NIGERIA.

© 2023, IRJET

|

Impact Factor value: 8.226

|

ISO 9001:2008 Certified Journal

|

Page 728