YOLO11-DAL a multi-scale fusion network for target detection in UAV-based power line inspection
Abstract
Reliable detection of transmission-line components and defects in unmanned aerial vehicle (UAV) imagery supports preventive maintenance. Repeated downsampling, however, removes fine spatial detail, while limited interaction between feature levels hinders small-object detection. We developed YOLO11-DAL, a compact detector based on YOLO11n, to address these limitations. C3k2-LEGM integrates local structure with global context in two lower-backbone blocks. Attentional Scale Sequence Fusion (ASF) aggregates P2–P5 features, while a Dynamic Aligned Head (DAH) coordinates classification and localisation. On a seven-class UAV power-line dataset, YOLO11-DAL achieved 86.2% mAP50 and 57.7% mAP50:95. These values exceeded the YOLO11n baseline by 3.3 and 2.5% points, respectively. The parameter count decreased from 2.58 million to 2.34 million. A prototype using a ZED camera and Jetson TX2 integrated image acquisition, inference and result visualisation. End-to-end latency was not measured, so the prototype establishes functional integration rather than verified real-time performance. The results support coordinated feature fusion and task-specific prediction as a practical design for compact UAV inspection detectors.
Related
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BibTeX
@article{wu2026yolo11dal,
title = {{YOLO11-DAL a multi-scale fusion network for target detection in UAV-based power line inspection}},
author = {Taotao Wu and Zhonghua Liu},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-76269-3},
url = {https://www.nature.com/articles/s41598-026-76269-3},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - YOLO11-DAL a multi-scale fusion network for target detection in UAV-based power line inspection
AU - Taotao Wu
AU - Zhonghua Liu
PY - 2026
DA - 2026/10/17
JO - Scientific Reports
DO - 10.1038/s41598-026-76269-3
UR - https://www.nature.com/articles/s41598-026-76269-3
AB - Reliable detection of transmission-line components and defects in unmanned aerial vehicle (UAV) imagery supports preventive maintenance. Repeated downsampling, however, removes fine spatial detail, while limited interaction between feature levels hinders small-object detection. We developed YOLO11-DAL, a compact detector based on YOLO11n, to address these limitations. C3k2-LEGM integrates local structure with global context in two lower-backbone blocks. Attentional Scale Sequence Fusion (ASF) aggregates P2–P5 features, while a Dynamic Aligned Head (DAH) coordinates classification and localisation. On a seven-class UAV power-line dataset, YOLO11-DAL achieved 86.2% mAP50 and 57.7% mAP50:95. These values exceeded the YOLO11n baseline by 3.3 and 2.5% points, respectively. The parameter count decreased from 2.58 million to 2.34 million. A prototype using a ZED camera and Jetson TX2 integrated image acquisition, inference and result visualisation. End-to-end latency was not measured, so the prototype establishes functional integration rather than verified real-time performance. The results support coordinated feature fusion and task-specific prediction as a practical design for compact UAV inspection detectors.
SN - 2045-2322
ER -