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Carvalho, J. P. M., Stefenon, S. F., Leithardt, V. R. Q., Seman, L. O., Yow, K.-C. & Paz Santana, J. F. de. (2026). Input attention, squeeze and excitation, and spatial transformer of YOLO for fault detection using UAV. Ain Shams Engineering Journal. 17 (3)

J. P. Carvalho et al.,  "Input attention, squeeze and excitation, and spatial transformer of YOLO for fault detection using UAV", in Ain Shams Engineering Journal, vol. 17, no. 3, 2026

@article{carvalho2026_1772731662457,
	author = "Carvalho, J. P. M. and Stefenon, S. F. and Leithardt, V. R. Q. and Seman, L. O. and Yow, K.-C. and Paz Santana, J. F. de.",
	title = "Input attention, squeeze and excitation, and spatial transformer of YOLO for fault detection using UAV",
	journal = "Ain Shams Engineering Journal",
	year = "2026",
	volume = "17",
	number = "3",
	doi = "10.1016/j.asej.2026.104067",
	url = "https://www.sciencedirect.com/journal/ain-shams-engineering-journal"
}

TY  - JOUR
TI  - Input attention, squeeze and excitation, and spatial transformer of YOLO for fault detection using UAV
T2  - Ain Shams Engineering Journal
VL  - 17
IS  - 3
AU  - Carvalho, J. P. M.
AU  - Stefenon, S. F.
AU  - Leithardt, V. R. Q.
AU  - Seman, L. O.
AU  - Yow, K.-C.
AU  - Paz Santana, J. F. de.
PY  - 2026
SN  - 2090-4479
DO  - 10.1016/j.asej.2026.104067
UR  - https://www.sciencedirect.com/journal/ain-shams-engineering-journal
AB  - The detection of faults in insulators is important to guarantee the continuous supply of electricity. To identify faults in these components, various object detection methods based on deep learning have been explored. This paper investigates architectural enhancements to the You Only Look Once (YOLO) framework for fault detection in electrical power grid insulators. Three structural variants are proposed: the Input Attention Transformer (IAT-YOLO) for spatial feature refinement, Squeeze-and-Excitation (SAE-YOLO) modules for channel recalibration, and Spatial Transformer Networks (STN-YOLO) for geometric alignment. Experiments were conducted on a publicly available insulator dataset from Unmanned Aerial Vehicles (UAVs), comprising seven defect categories, including pollution, breakage, and flashover damage. Results demonstrate that STN-YOLO and SAE-YOLO consistently improve generalization and robustness, achieving mAP values of up to 0.995 for specific classes. The findings highlight the effectiveness of integrating attention mechanisms and spatial transformations to enhance YOLO-based detection, contributing to improved automated inspection of the power grid.
ER  -