Real-Time Object Detection and Distance Mapping

Abstract

This initiative involves an advanced AI-driven object detection system that employs the YOLO (You Only Look Once) deep learning framework to recognize and monitor objects in real-time from both images and videos. It comprises multiple Python scripts, including object_detection.py, real_time.py, and video_with_distance.py, which facilitate the identification of objects in photographs, live video feeds, and the estimation of their distances. The system is equipped with pre-trained YOLO model weights (yolov8m.pt, yolov8n.pt), enabling rapid and effective object recognition. Additionally, it provides sample videos (33.mp4, 34.mp4) and images (bus.jpg, output_detected.jpg) to evaluate the performance of the detection model. A COCO dataset file (coco.txt) is included, signifying that the model has been trained to identify a diverse range of common objects. Furthermore, other Python scripts (conv.py, test4.py) appear to be utilized for data conversion, testing, or enhancing the system\'s capabilities. This project holds significant potential for applications in real-time surveillance, autonomous vehicles, intelligent traffic management, security systems, and AI-driven automation, thereby improving the efficiency of object detection and tracking across various practical scenarios

Introduction

Project Summary

This project focuses on developing a real-time object detection system using the YOLOv8 (You Only Look Once) deep learning model. The goal is to accurately detect and track objects in both images and live video streams, while also estimating object distances, supporting applications like autonomous vehicles, security surveillance, and smart traffic systems.

Dataset

The COCO (Common Objects in Context) dataset is used, known for its rich variety of real-world annotated images. It supports tasks like object detection, segmentation, and captioning. The dataset’s realistic complexity helps train models to perform well in practical environments.

Literature Review

Studies from 2021–2025 highlight ongoing advances in autonomous vehicle technology, particularly in:

• Object detection in adverse conditions (e.g., fog, rain)

• Sensor fusion (LiDAR, radar, cameras)

• Deep learning for localization

• Challenges like real-time processing, cost, adversarial attacks, and ethical concerns

These findings underscore the importance of AI robustness, safety, and scalability.

Methodology

1. Data Acquisition: Uses pre-trained YOLOv8 models (yolov8m.pt, yolov8n.pt) with image and video inputs.

2. Preprocessing: Input is resized and normalized; video is processed frame-by-frame using OpenCV.

3. Model Implementation: Multiple Python scripts enable static, real-time, and distance-aware object detection.

4. Post-processing: Adds bounding boxes and filters predictions by confidence to improve clarity.

5. Evaluation & Optimization: Performance is measured using precision, recall, and MAP; model size vs. speed is balanced.

6. Deployment: Designed for real-time applications on both standard and edge devices (e.g., Raspberry Pi, Jetson), with future plans for web/mobile integration.

Results & Discussion

Tests show high accuracy in both image and video scenarios. For instance:

• In "bus2.jpg", five people were detected (confidence: 0.39–0.91).

• Two buses and a truck were identified, but lower scores for some objects (e.g., 0.27) suggest occasional misclassification or occlusion effects.

• Real-time detection was smooth and efficient, with added distance estimation enhancing spatial understanding.

Conclusion

The Autonomous Navigation & Safety System (Cars/Objects) utilizing the YOLO model represents a cutting-edge solution aimed at improving personal safety through advanced technological integration. This system merges hand gesture recognition, facial emotion detection, voice activation, GPS tracking, and real-time live streaming to function as an automated emergency response assistant, delivering prompt and effective safety interventions. Leveraging deep learning, computer vision, and real-time communication, it is capable of identifying distress signals and responding without delay. The emergency alert feature guarantees swift assistance for users, while live video streaming and GPS tracking provide immediate updates for responders. With its diverse capabilities, the system serves as a dependable safety net for individuals in precarious situations or emergencies. Future enhancements could include the integration of AI-driven behavioral analysis, IoT connectivity, and cloud-based data storage to further improve its efficiency and scalability. This initiative underscores the potential of AI in enhancing safety and illustrates the significant role technology can play in safeguarding lives and fostering a safer environment.

References

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Copyright

Copyright © 2025 Saketh Kinthali, Sai Krishna Pudi, Dr Raghavendra V. 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.