Aero-engine ablation detection is a critical task in aircraft health management, yet existing rotation-based object detection methods often face challenges of high computational complexity and insufficient local feature extraction. This paper proposes an improved YOLOv11 algorithm incorporating Context-guided Large-kernel attention and Rotated detection head, called CLR-YOLOv11. The model achieves synergistic improvement in both detection efficiency and accuracy through dual structural optimization, with its innovations primarily embodied in the following three tightly coupled strategies: (1) Targeted Data Preprocessing Pipeline Design: To address challenges such as limited sample size, low overall image brightness, and noise interference, we designed an ordered data augmentation and normalization pipeline. This pipeline is not a mere stacking of techniques but strategically enhances sample diversity through geometric transformations (random flipping, rotation), hybrid augmentations (Mixup, Mosaic), and pixel-value transformations (histogram equalization, Gaussian filtering). All processed images subsequently undergo Z-Score normalization. This order-aware pipeline design effectively improves the quality, diversity, and consistency of the input data. (2) Context-Guided Feature Fusion Mechanism: To overcome the limitations of traditional Convolutional Neural Networks in modeling long-range contextual dependencies between ablation areas and surrounding structures, we replaced the original C3k2 layer with the C3K2CG module. This module adaptively fuses local textural details with global semantic information through a context-guided mechanism, enabling the model to more accurately understand the gradual boundaries and spatial context of ablation regions. (3) Efficiency-Oriented Large-Kernel Attention Optimization: To expand the receptive field while strictly controlling the additional computational overhead introduced by rotated detection, we replaced the C2PSA module with the C2PSLA module. By employing large-kernel decomposition and a spatial selective focusing strategy, this module significantly reduces computational load while maintaining multi-scale feature perception capability, ensuring the model meets the demands of high real-time applications. Experiments on a self-built aero-engine ablation dataset demonstrate that the improved model achieves 78.5% mAP@0.5:0.95, representing a 4.2% improvement over the YOLOv11-obb which model without the specialized data augmentation. This study provides an effective solution for high-precision real-time aviation inspection tasks.

Efficient detection of aircraft surface defects (ASD) is a cornerstone of aviation safety. However, ASD detection is challenged by microscopic defect scales, extremely low contrast, and severe background interference. This paper proposes the Multi-Scale Discriminative Edge Enhancement Network (DEE-Net) based on an improved YOLO11. First, to mitigate feature dissipation of tiny defects, a lossless reassembly mechanism using space-to-depth convolution (SPD-Conv) is introduced, safeguarding sub-pixel topological information through space-to-depth conversion. Second, an adaptive selective edge-enhancement (ASE) module, integrating a dual-domain selection mechanism (DSM), is designed to suppress non-target redundant information on the fuselage skin. Finally, a Wise-CIoU loss function with a non-monotonic focusing mechanism is introduced to enhance localization stability under stringent IoU thresholds. Experimental results demonstrate that DEE-Net outperforms the baseline, improving mAP50 by 7.15% and mAP50-95 by 2.43%. To provide a more reliable evaluation, a 5-fold cross-validation experiment is further conducted on the original non-augmented images, and the results are reported as mean ± standard deviation. The cross-validation results provide a more conservative estimate and indicate that the proposed method achieves competitive performance across different data partitions.

Homography estimation of infrared and visible light images is a key visual technique that enables drones to perceive their environment and perform autonomous localization in low-altitude environments. Its potential lies in integration with edge computing and 5G technologies, enabling real-time control of drones within air–ground integrated networks. However, research on homography estimation techniques for low-altitude dynamic viewpoints remains scarce. Additionally, images in low-altitude scenarios suffer from issues such as blurring and jitter, presenting new challenges for homography estimation tasks. To address these issues, this paper proposes a light-weight homography estimation method, LFHomo, comprising two components: two anti-blurring feature extractors with non-shared parameters and a lightweight homography estimator, LFHomoE. The anti-blurring feature extractors introduce in-verse residual layers and feature displacement modules to capture sufficient contextual information in blurred regions and to enable lossless and rapid propagation of feature information. In addition, a spatial-reduction-based channel shuffle and spatial joint attention module is designed to suppress redundant features introduced by lossless transmission, allowing efficient extraction and refinement of informative features at low computational cost. The homography estimator LFHomoE adopts a CNN–GNN hybrid architecture to efficiently model geometric relationships between cross-modal features and to achieve fast prediction of homography matrices. Meanwhile, we construct and annotate an unregistered infrared and visible image dataset from drone perspectives for model training and evaluation. Experimental results show that LFHomo maintains great registration accuracy while significantly reducing model size and inference time.

With the rapid development of the aviation industry, engines operate under extreme conditions of high temperature, high pressure, and high vibration, making them prone to surface damage such as ablation. Ablation not only affects the structural integrity of engine components but also threatens flight safety, making efficient and accurate detection of paramount importance. Traditional detection methods rely on manual visual inspection and non-destructive testing, which suffer from high subjectivity and low efficiency. In recent years, deep learning has achieved significant progress in industrial defect detection. However, conventional CNN-and Transformer-based architectures still suffer from substantial computational overhead and inadequate boundary segmentation accuracy in aero-engine ablation detection. This paper proposes a novel dual-pathway network Visual State-Space Residual Neural Network (VSS-ResNet) based on Mamba that combines Visual State Space (VSS) modules with ResNet50. This architecture leverages the global modeling capability of VSS modules and the local feature extraction capability of CNNs, effectively enhancing the accuracy and robustness of ablation boundary detection with the support of multi-scale feature fusion modules. Experimental results demonstrate that the proposed method achieves superior performance in mIoU, mPA, and Acc compared to mainstream segmentation models such as U-Net, Pyramid Scene Parsing Network (PSPNet), and DeepLab V3+ on a self-constructed engine endoscopic ablation dataset, validating its potential in intelligent aero-engine inspection.

Instrument reading detection in industrial scenarios poses significant challenges due to reading contour distortion caused by perspective transformation in the instrument images. However, existing methods fail to accurately read the display automatically due to incorrect labeling of the target box vertices, which arises from the vertex entanglement problem. To address these challenges, a novel Quadrilateral Contour Disentangled Detection Network (QCDNet) is proposed in this paper, which utilizes the quadrilateral disentanglement idea. First, a Multi-scale Feature Pyramid Network (MsFPN) is proposed for effective feature extraction to improve model accuracy. Second, we propose a Polar Coordinate Decoupling Representation (PCDR), which models each side of the instrument contour using polar coordinates. Additionally, a loss function for the polar coordinate parameters is designed to aid the PCDR in more effectively decoupling the instrument reading contour. Finally, the experimental results on the instrument dataset demonstrate that QCDNet outperforms existing quadrilateral detection algorithms, with improvements of 4.07%, 1.8%, and 2.89% in Precision, Recall, and F-measure, respectively. These results confirm the effectiveness of QCDNet for instrument reading detection tasks.