Efficient bridge damage detection using a lightweight attention-based modeling framework

Currently, real-time assessment of surface damage to bridges is crucial for ensuring infrastructure safety. Unfortunately, existing methods often present a challenge: overly complex computational models are incompatible with systems that have limited resources, while lightweight models struggle to achieve sufficient detection accuracy. This task is further complicated by the diverse nature of bridge damages, such as cracks, exposed reinforcement, and efflorescence, as well as the challenges of data acquisition under varied conditions from sources like unmanned aerial vehicles and specialized datasets. This work presents an efficient framework developed to improve such applications. The Lightweight Feature Enhancement and Triplet Attention Network for Bridge Damage Detection includes: (1) a multi-scale feature learning module, (2) a slim-neck-based optimized feature pyramid integration module, and (3) a triplet attention-based damage detector module; (1) extracts multi-scale representations of bridge surface features, (2) enhances multi-scale feature integration for lightweight computation, while maintaining accuracy, and (3) optimizes the framework with a three-branch structure for cross-latitude interaction, reducing the importance of irrelevant features. Extensive experiments on the MCDS and CODEBRIM datasets demonstrated its advantages: a (Formula presented.) increase in Mean Average Precision, a (Formula presented.) computational load reduction, and a 45 frames per second real-time performance. The model's computational complexity scales linearly with the input instances processed per unit time during inference.