Conventional pavement monitoring techniques have shortcomings such as high costs and time consumption

The gap

Evidence profile

Sourced from the conclusions and stated research gap of the source papers, classified as general, spanning 3 journals. Those papers have been cited 1 times in total.

Supporting evidence — 3 representative gaps

• Deep learning and image processing techniques for road damage detection using a smartphone: a pilot diagnostic study and a framework of research for a rural road in Egypt (2026) · Innovative Infrastructure Solutions · doi

  According to the results, the direct application of publicly trained road-damage detection models to Egyptian road conditions is difficult, and the performance that is achieved is insufficient for practical operational use. The results indicate that variation in the dataset alone is insufficient to ensure the precise identification of cross- domain road damage.

  generalconclusions
  Keywords: road damage insufficient according direct application publicly trained detection models egyptian conditions difficult performance achieved
• Application of Deep Learning for Pavement Monitoring: Movement towards Autonomous Future (2026) · Bulletin of Computational Intelligence · doi

  Conventional pavement monitoring techniques have shortcomings such as high costs and time consumption. There is a need for automatic and data-driven techniques for pavement assessment and monitoring. The study aims to address this gap by reviewing state-of-the-art deep learning techniques for pavement distress detection.

  generalstated research gapevidence 5/5
  Keywords: conventional pavement monitoring techniques have shortcomings high costs
• Road damage detection method based on UAV imagery and YOLO-SCX (2026) · Frontiers in Built Environment · cited 1× · doi

  The lack of computationally efficient object detection architectures for automated road damage detection. The need for structural optimizations to address small target dimensions and complex environmental backgrounds. The importance of integrating multiple modules to achieve high-precision detection in complex aerial scenarios.

  generalstated research gapevidence 5/5
  Keywords: lack computationally efficient object detection architectures automated road

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