Survey on Target Detection Using Computer Vision and Machine Learning

Abstract

This paper presents a survey on Automatic Machine Gun system for military use, integrating Computer Vision and Machine Learning for real-time target detection and engagement. The system uses lightweight Convolutional Neural Networks (CNN) enhanced with the Ghost Convolution Module, which reduces redundant feature maps through cheap linear transformations, enabling faster inference with fewer parameters. Object detection is performed using optimized YOLOv8 version YOLOv8m, while DeepSORT handles tracking on the priority basis. The setup is powered by embedded platforms like Arduino, controlling servo-based aiming mechanisms. The system supports thermal imaging for low-light conditions and applies transfer learning for robust performance across various environments. Safety features and manual overrides ensure secure, ethical deployment in combat scenarios.

Introduction

1. Introduction

• Warfare in the 21st century has shifted from traditional battlefields to include cyber, digital, and autonomous domains.

• AI and Machine Learning (ML) are revolutionizing military operations, enabling machines to perceive, analyze, and act autonomously.

• Automation provides benefits like 24/7 vigilance, real-time decision-making, reduced human error, and increased operational efficiency.

2. Motivation

• Soldiers in high-risk zones (e.g., borders, combat areas) face constant threats.

• Human limitations (fatigue, stress) can lead to delays or errors in threat detection.

• Real-time AI-powered systems reduce casualties, enable rapid threat response, and improve mission success.

• Tools like YOLO, TensorFlow, OpenCV, Arduino, and Raspberry Pi have made it feasible to develop low-cost, high-performance autonomous defense systems.

3. Key Technologies Used

• YOLOv8: High-speed, accurate object detection model.

• OpenCV: Real-time image processing and camera integration.

• Arduino: Controls servos and firing mechanisms.

• PID Control: Ensures precision aiming of weapons.

• Python-Arduino Communication: Enables real-time control of hardware based on ML outputs.

• Custom Datasets: Used for training models on military-specific targets.

4. Literature Review Highlights

5. Methodology

1. Image Acquisition: Using cameras to capture visuals of the environment.

2. Pre-processing: Resize, normalize, and enhance images.

3. Feature Extraction & Detection:

   • Use of YOLOv8 for object detection (people, vehicles, drones).

   • Detection based on motion, shape, heat, or color.

4. Servo Control & Shooting:

   • Arduino interfaces with servo motors to aim.

   • Automatic firing when target is locked.

   • PID controllers ensure precise aiming.

5. Communication:

   • Python sends commands to Arduino for firing via serial communication.

6. Results and Discussion

• The system successfully detects, tracks, and classifies targets in real-time.

• Integration with servo-controlled machine guns allows for automated and precise firing.

• Supports functions like:

  • Multi-target tracking

  • Motion prediction

  • Thermal signature detection

• Key benefits:

  • Enhanced battlefield accuracy

  • Minimized collateral damage

  • Reduced human involvement in direct combat

• Emphasizes the importance of ethics and fail-safes to prevent unintended harm or malfunction.

Conclusion

In conclusion, the integration of computer vision and machine learning into automated defense systems represents a major leap forward in modern military technology. These systems bring unmatched speed and accuracy to threat detection and engagement, reducing reliance on human intervention in high-risk zones. By leveraging real-time image processing and AI-driven classification, such platforms offer the capability to respond to threats with greater precision than traditional human-operated systems. ` The implementation of automatic machine gun control based on intelligent vision systems not only enhances combat readiness but also contributes to reduced soldier casualties. These systems can operate continuously in harsh environments, identify multiple targets simultaneously, and make split-second decisions that are crucial during enemy encounters. Additionally, machine learning enables the system to improve over time, adapting to new threats and tactics based on battlefield data, thus increasing its effectiveness with every deployment. However, while the technical benefits are substantial, the deployment of autonomous weapon systems must be approached cautiously. Ethical considerations, rules of engagement, and international laws must govern their use to prevent accidental harm and misuse. Going forward, the balance between automation and human oversight will be essential to ensure such technologies serve as tools for defence rather than unregulated aggression. With responsible development, these systems can redefine how military defence is managed in the 21st century.

References

In conclusion, the integration of computer vision and machine learning into automated defense systems represents a major leap forward in modern military technology. These systems bring unmatched speed and accuracy to threat detection and engagement, reducing reliance on human intervention in high-risk zones. By leveraging real-time image processing and AI-driven classification, such platforms offer the capability to respond to threats with greater precision than traditional human-operated systems. ` The implementation of automatic machine gun control based on intelligent vision systems not only enhances combat readiness but also contributes to reduced soldier casualties. These systems can operate continuously in harsh environments, identify multiple targets simultaneously, and make split-second decisions that are crucial during enemy encounters. Additionally, machine learning enables the system to improve over time, adapting to new threats and tactics based on battlefield data, thus increasing its effectiveness with every deployment. However, while the technical benefits are substantial, the deployment of autonomous weapon systems must be approached cautiously. Ethical considerations, rules of engagement, and international laws must govern their use to prevent accidental harm and misuse. Going forward, the balance between automation and human oversight will be essential to ensure such technologies serve as tools for defence rather than unregulated aggression. With responsible development, these systems can redefine how military defence is managed in the 21st century.

Copyright

Copyright © 2025 Prajwal P, Dr. Kamalakshi Naganna, Syed Aqib Umer , Mitan Hiremath, Rohan M Naik. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.