Article · IEEE Access · January 1, 2026

Adaptive Confidence-Based Fire and Smoke Detection: A Two-Stage YOLO Framework With Selective Segmentation Validation for Real-Time Surveillance Systems

Bekbol Sultankulov, Marat Nurtas, Daniker Chepashev, Elmira Zhilkibayeva

Abstract

Automated fire and smoke detection in industrial surveillance systems represents a safety-critical computer vision challenge where both missed detections and false alarms carry significant operational consequences. Single-stage YOLO-based detectors achieve competitive inference speeds but generate false positive detections at operationally disruptive rates under adverse industrial conditions, including partial occlusion, low illumination, smoke-induced visual degradation, steam emissions, and reflective surfaces characteristic of petrochemical and manufacturing environments. This paper proposes AC-YOLO, an adaptive confidence-based two-stage detection and validation framework that integrates a fine-tuned YOLOv9c primary detector with selective segmentation-based validation to suppress false positives while preserving real-time throughput. The core contribution is a three-tier confidence-routing mechanism that partitions detections into directly accepted high-confidence outputs, empirically selected intermediate-confidence detections subjected to pixel-level segmentation validation, and discarded low-confidence outputs. The intermediate confidence interval of 0.50-0.85 was identified through ablation as the stratum containing 78.3% of all false positive detections generated by the single-stage baseline. Two segmentation validation backends are evaluated within the framework: YOLO26-seg, optimized for real-time deployment, and SAM 3 (Segment Anything Model 3, Meta AI), a foundation model supporting concept-level promptable segmentation. Multi-criteria validation applies pixel occupancy ratio, shape compactness, and mask confidence scoring under a conjunctive decision rule, with each criterion contributing an independently measurable reduction in false positive rate. The Stage 2 validator is trained, and the framework evaluated, on a heterogeneous dataset of 35,412 annotated images spanning public sources and four active Kazakhstani industrial facilities. Experimental evaluation on a 3,542-image test set demonstrates that AC-YOLO with the YOLO26-seg backend achieves 88.4% precision and 74.1% recall at 26.8 FPS, reducing the false positive rate from 18.7% to 10.2% relative to the single-stage YOLOv9c baseline while maintaining throughput above the 25 FPS real-time threshold. Comparative evaluation of YOLO26-seg and SAM 3 reveals a well-defined accuracy-latency trade-off: SAM 3 achieves a lower false positive rate (7.8%) at 4.1 FPS, establishing it as appropriate for offline validation, while YOLO26-seg satisfies both accuracy and real-time constraints for live deployment. Production deployment at all four facilities over a 30-day period yielded a false alarm rate of 19.0%, representing a 50.6% relative reduction over the single-stage baseline. All fire events confirmed through operator-verified alarm logs were successfully detected during the monitored period, although this metric cannot rule out events missed by both the system and human operators.

Fire Detection and Safety SystemsAdvanced Neural Network ApplicationsIndustrial Vision Systems and Defect Detectionfire detectionsmoke detectionindustrial surveillance

Adaptive Confidence-Based Fire and Smoke Detection: A Two-Stage YOLO Framework With Selective Segmentation Validation for Real-Time Surveillance Systems

Abstract

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