arXiv:2604.27617v2 Announce Type: replace-cross Abstract: With the widespread application of Unmanned Aerial Vehicles (UAVs) in bridge structural health monitoring, deep learning-based automatic crack detection has become a major research focus. However, practical UAV inspections still face four key challenges: weak crack features, degraded imaging conditions, severe class imbalance, and limited computational resources for practical UAV inspection workflows. To address these issues, this paper proposes a unified lightweight convolutional neural network framework composed of four synergistic co
The increasing adoption of UAVs for infrastructure inspection and the rapid advancements in lightweight AI models for edge devices make real-time, robust solutions critical.
This development addresses key practical challenges in autonomous infrastructure monitoring, enhancing efficiency and reliability, which has significant economic and safety implications for civil engineering.
The ability to perform real-time, reliable crack detection on UAVs with limited computational resources makes automated bridge inspection more practical and scalable.
- · Civil engineering firms
- · UAV manufacturers
- · Infrastructure owners
- · AI model developers
- · Manual inspection companies
- · Outdated bridge infrastructure
Wider deployment of autonomous drone-based inspection systems for critical infrastructure.
Reduced maintenance costs and improved safety records for bridges and other structures.
Potential for new insurance models based on real-time structural health monitoring data.
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Read at arXiv cs.AI