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Real time blood detection in CCTV surveillance using attention enhanced InceptionV3.

Adnan Khalil¹, Fakhre Alam¹, Dilawar Shah²

¹Department of Computer Science and Information Technology, University of Malakand, Dir, Pakistan.

Scientific Reports

|August 8, 2025

Summary

This summary is machine-generated.

This study introduces a deep learning framework for real-time blood detection in CCTV footage. The advanced model achieves over 94% accuracy, enhancing public safety surveillance.

Keywords:

BloodNet Event detection Feature fusion Indoor blood classification Multi-scale attention mechanism

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Area of Science:

Computer Vision
Artificial Intelligence
Public Health Surveillance

Background:

Accurate blood detection in CCTV footage is crucial for public safety and emergency response.
Existing methods struggle with challenging conditions like low visibility and motion blur.

Purpose of the Study:

To develop a real-time deep learning framework for accurate blood detection in surveillance videos.
To enhance the model's ability to identify subtle blood patterns under adverse conditions.

Main Methods:

Utilized the InceptionV3 architecture combined with Convolutional Block Attention Modules.
Developed a novel attention module to focus on small blood patterns.
Trained and evaluated the model on a custom dataset of over 9500 annotated CCTV images.

Main Results:

Achieved a detection accuracy of 94.5%.
Reported precision, recall, and F1-scores exceeding 94%.
Outperformed baseline methods in identifying blood traces under various conditions.

Conclusions:

The proposed deep learning framework effectively detects blood in real-world CCTV surveillance.
This offers a practical and scalable solution for improving public health and safety monitoring.
The study provides an open-source dataset and code for further research.