Computer vision‐based real‐time cable safety assessment under vehicle‐induced bridge fires

Vehicle-induced fires present a critical risk to cable-supported bridges, where the integrity of cable components is especially vulnerable. However, conventional monitoring solutions face significant limitations: infrared cameras are often economically prohibitive, and standard smoke detectors are ineffective in open bridge environments. To address these challenges, this paper proposes a multi-stage computer vision framework that utilizes existing visual surveillance infrastructure for real-time fire detection and preliminary cable safety assessment. The proposed system integrates a you only look once v11-m model for accurate vehicle detection, a BoT-SORT tracker with re-identification (Re-ID) capabilities to maintain target consistency through visual obstructions such as smoke, and a ResNet-50 classifier for vehicle-centric fire identification. The framework's novelty lies in the demonstrated synergistic operation of these components across various scenarios, particularly under actual fire conditions. The integration of the Re-ID module proves essential for eliminating false alarms by preserving target identity, while the vehicle-centric approach directly associates fire events with specific vehicles and their tracking identifiers. This linkage provides the fundamental basis for real-time safety evaluation of adjacent cables. Consequently, the framework establishes a cost-effective, readily deployable, and scalable solution for bridge monitoring, offering management authorities a practical tool for immediate fire detection and instant structural assessment.