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<p>Accurate detection of blood in CCTV surveillance footage is critical for timely response to medical emergencies, violent incidents, and public safety threats. This study proposes a real-time deep learning framework that combines the InceptionV3 architecture with Convolutional Block Attention Modules to enhance spatial and channel-level feature discrimination. The model is further optimized through a <i>proposed attention</i> module that intensifies attention to small and minute blood-related patterns, even under challenging conditions such as occlusions, motion blur, and low visibility. A dedicated benchmark dataset comprising over 9500 manually annotated CCTV images captured under diverse lighting and environmental scenarios is developed for model training and evaluation. It achieves a detection accuracy of 94.5%, with precision, recall, and F1-scores all exceeding 94%, outperforming baseline methods. These results demonstrate the effectiveness in accurately identifying blood traces in real-world surveillance footage, offering a practical and scalable solution for enhancing public health and safety monitoring. All code and data are available at <a href="https://github.com/irshadkhalil23/bloodNet_model">https://github.com/irshadkhalil23/bloodNet_model</a>.</p>

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Real time blood detection in CCTV surveillance using attention enhanced InceptionV3

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月7日

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  • Adnan Khalil,
  • Fakhre Alam,
  • Dilawar Shah,
  • Irshad khalil,
  • Shujaat Ali,
  • Muhammad Tahir

摘要

Accurate detection of blood in CCTV surveillance footage is critical for timely response to medical emergencies, violent incidents, and public safety threats. This study proposes a real-time deep learning framework that combines the InceptionV3 architecture with Convolutional Block Attention Modules to enhance spatial and channel-level feature discrimination. The model is further optimized through a proposed attention module that intensifies attention to small and minute blood-related patterns, even under challenging conditions such as occlusions, motion blur, and low visibility. A dedicated benchmark dataset comprising over 9500 manually annotated CCTV images captured under diverse lighting and environmental scenarios is developed for model training and evaluation. It achieves a detection accuracy of 94.5%, with precision, recall, and F1-scores all exceeding 94%, outperforming baseline methods. These results demonstrate the effectiveness in accurately identifying blood traces in real-world surveillance footage, offering a practical and scalable solution for enhancing public health and safety monitoring. All code and data are available at https://github.com/irshadkhalil23/bloodNet_model.