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Fire and Smoke Detection Using Capsule Network


Video surveillance and image processing approaches are widely used nowadays for security and safety purposes. Such systems are also effective for smoke and fire detection and are one of the safety techniques to eliminate the drastic situation in their early stage. Detection of fire and smoke incidents in their early stages is of utmost importance. The conventional detectors having several limitations are now being replaced with intelligent video-based sensors. Training an efficient fire and smoke detection system using smart video-based detectors required a massive amount of annotated data describing unique fire patterns. This work presents a real-time video-based fire and smoke detection in its early stages while suppressing false alarms due to varying illumination (i.e., weather conditions), burning patterns, fog, cloud, and distinctive characteristics of fire and smoke, etc. The model is trained using Capsule Network-based architecture on different fire and smoke patterns obtained from publicly available datasets. Experimental results showed that the proposed architecture improved significantly in terms of accuracy, final model size, and false-positive rate on both binary and multiclass classification of fire and smoke compared with various state-of-the-art studies. These results validate the proposed architecture's generalizability and suitability for intelligent video-based fire and smoke detection using CCTV cameras.

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Correspondence to Usama Ijaz Bajwa.

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Khan, R.A., Hussain, A., Bajwa, U.I. et al. Fire and Smoke Detection Using Capsule Network. Fire Technol 59, 581–594 (2023).

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  • Capsule network
  • CCTV surveillance videos
  • Deep learning
  • Fire detection
  • FPR
  • Smoke detection
  • Model size