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Journal = Drones
Section = Artificial Intelligence in Drones (AID)

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29 pages, 1494 KiB  
Article
An Aircraft Skin Defect Detection Method with UAV Based on GB-CPP and INN-YOLO
by Jinhong Xiong, Peigen Li, Yi Sun, Jinwu Xiang and Haiting Xia
Drones 2025, 9(9), 594; https://doi.org/10.3390/drones9090594 - 22 Aug 2025
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Abstract
To address the problems of low coverage rate and low detection accuracy in UAV-based aircraft skin defect detection under complex real-world conditions, this paper proposes a method combining a Greedy-based Breadth-First Search Coverage Path Planning (GB-CPP) approach with an improved YOLOv11 architecture (INN-YOLO). [...] Read more.
To address the problems of low coverage rate and low detection accuracy in UAV-based aircraft skin defect detection under complex real-world conditions, this paper proposes a method combining a Greedy-based Breadth-First Search Coverage Path Planning (GB-CPP) approach with an improved YOLOv11 architecture (INN-YOLO). GB-CPP generates collision-free, near-optimal flight paths on the 3D aircraft surface using a discrete grid map. INN-YOLO enhances detection capability by reconstructing the neck with the BiFPN (Bidirectional Feature Pyramid Network) for better feature fusion, integrating the SimAM (Simple Attention Mechanism) with convolution for efficient small-target extraction, as well as employing RepVGG within the C3k2 layer to improve feature learning and speed. The model is deployed on a Jetson Nano for real-time edge inference. Results show that GB-CPP achieves 100% surface coverage with a redundancy rate not exceeding 6.74%. INN-YOLO was experimentally validated on three public datasets (10,937 images) and a self-collected dataset (1559 images), achieving mAP@0.5 scores of 42.30%, 84.10%, 56.40%, and 80.30%, representing improvements of 10.70%, 2.50%, 3.20%, and 6.70% over the baseline models, respectively. The proposed GB-CPP and INN-YOLO framework enables efficient, high-precision, and real-time UAV-based aircraft skin defect detection. Full article
(This article belongs to the Section Artificial Intelligence in Drones (AID))
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