An Aircraft Skin Defect Detection Method with UAV Based on GB-CPP and INN-YOLO

Abstract

Highlights: What are the main findings? In the task of using drones for aircraft skin inspection, a coverage path planning method based on greedy algorithm and breadth-first search (GB-CPP) was proposed. The proposed INN-YOLO algorithm, based on YOLOv11, demonstrated superior performance in comparative experiments on three public datasets. What is the implication of the main finding? Proposes a collaborative framework integrating geometry-guided coverage path planning with a lightweight detection network to optimize UAV inspection routes and enable real-time defect identification, thereby enhancing operational efficiency and intelligence levels. The INN-YOLO detection model meets the onboard low-latency requirements, supporting immediate decision-making and feedback during the inspection process. The proposed collaborative framework promotes a closed-loop system of “precise path planning—efficient image acquisition—onboard real-time recognition,” providing a replicable industrial solution for automated inspection of large-scale infrastructure such as aviation facilities. 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.