Expanding the current dataset to include underrepresented
Research gap analysis derived from 3 computer_science papers in our local library.
The gap
Expanding the current dataset to include underrepresented defect categories, - Refining the feature extraction process to capture more granular textural details, - Integrating deep learning models into automated aerial inspection frameworks
Evidence profile
Sourced from the future-work section and stated research gap and conclusions of the source papers, classified as general, spanning 2 journals.
Research trend
Established — well-defined area with open sub-problems.
Supporting evidence — 3 representative gaps
- AI-based visual analysis of photovoltaic panels for fault detection and maintenance support (2026) · Scientific Reports · doi
Expanding the current dataset to include underrepresented defect categories, - Refining the feature extraction process to capture more granular textural details, - Integrating deep learning models into automated aerial inspection frameworks, - Developing lightweight architectures for resource-constrained edge devices
generalfuture-work sectionKeywords: expanding current dataset include underrepresented defect categories refining - Boundary-Sensitive Hybrid Attention Network for Multi-Scale Crack Fine Segmentation (2026) · Sensors · doi
Traditional methods are hindered by weak contrast, background interference, and multi-scale crack structures. Existing deep learning models face a serious 'scale-efficiency' paradox in concrete surface damage detection. There is a need for a scalable and reliable solution for automated infrastructure monitoring and defect detection.
generalstated research gapKeywords: traditional methods hindered weak contrast background interference multi-scale - A hybrid approach based on deep feature extraction and machine learning classification for structural damage detection in concrete structures (2026) · Scientific Reports · doi
Future investigations will focus on optimizing these hybrid structures for edge-computing devices and expanding the dataset to include multi-scale damage types in diverse environmental settings to further enhance urban resilience. Future investigations may focus on incorporating more deep learning structures and optimization strategies to improve crack detection.
generalconclusionsevidence 5/5Keywords: future investigations focus structures optimizing hybrid edge computing devices expanding dataset include multi scale damage
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