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

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 .