Sparse representation has been proven to be a very effective technique for various image restoration applications. In this paper, an improved sparse representation based method is proposed to detect and estimate defocus blur of imaging sensors. Considering the fact that the patterns usually vary remarkably across different images or different patches in a single image, it is unstable and time-consuming for sparse representation over an over-complete dictionary. We propose an adaptive domain selection scheme to prelearn a set of compact dictionaries and adaptively select the optimal dictionary to each image patch. Then, with nonlocal structure similarity, the proposed method learns nonzero-mean coefficients’ distributions that are much more closer to the real ones. More accurate sparse coefficients can be obtained and further improve the performance of results. Experimental results validate that the proposed method outperforms existing defocus blur estimation approaches, both qualitatively and quantitatively.

Defect detection in transparent materials typically relies on specific lighting conditions. However, through our work on defect detection for aircraft glass canopies, we found that using a single lighting condition often led to missed or false detections. This limitation arises from the optical properties of transparent materials, where certain defects only become sufficiently visible under specific lighting angles. To address this issue, we developed a dual-modal illumination system that integrates both forward and backward lighting to capture defect images. Additionally, we introduced the first dual-modal dataset for defect detection in aircraft glass canopies. Furthermore, we proposed an attention-based dual-branch modal fusion network (ADMF-Net) to enhance the detection process. Experimental results show that our system and model significantly improve the detection performance, with the dual-modal approach increasing the mAP by 5.6% over the single-modal baseline, achieving a mAP of 98.4%. Our research also provides valuable insights for defect detection in other transparent materials.

Aircraft ducts play an indispensable role in various systems of an aircraft. The regular inspection and maintenance of aircraft ducts are of great significance for preventing potential failures and ensuring the normal operation of the aircraft. Traditional manual inspection methods are costly and inefficient, especially under low-light conditions. To address these issues, we propose a new defect detection model called LESM-YOLO. In this study, we integrate a lighting enhancement module to improve the accuracy and recognition of the model under low-light conditions. Additionally, to reduce the model’s parameter count, we employ space-to-depth convolution, making the model more lightweight and suitable for deployment on edge detection devices. Furthermore, we introduce Mixed Local Channel Attention (MLCA), which balances complexity and accuracy by combining local channel and spatial attention mechanisms, enhancing the overall performance of the model and improving the accuracy and robustness of defect detection. Finally, we compare the proposed model with other existing models to validate the effectiveness of LESM-YOLO. The test results show that our proposed model achieves an mAP of 96.3%, a 5.4% improvement over the original model, while maintaining a detection speed of 138.7, meeting real-time monitoring requirements. The model proposed in this paper provides valuable technical support for the detection of dark defects in aircraft ducts.

Defects on large-scale structural surfaces can compromise integrity and pose safety hazards, highlighting the need for efficient automated inspection. UAVs provide a flexible and effective platform for such inspections, yet traditional vision-based methods often require high computational resources and show limited sensitivity to small defects, restricting practical UAV deployment. To address these challenges, we propose ESS-DETR, a lightweight and high-precision detection model designed for UAV-based surface inspection, built upon core modules: EMO-inspired lightweight backbone that integrates convolution and efficient attention mechanisms to reduce parameters; Scale-Decoupled Loss that adaptively balances targets of various sizes to enhance accuracy and robustness for small and irregular defect patterns frequently encountered in UAV imagery; and SPPELAN multi-scale fusion module that improves feature discrimination under complex reflections, shadows, and lighting variations typical of aerial inspection environments. Experimental results demonstrate that ESS-DETR reduces computational complexity from 103.4 to 60.5 GFLOPs and achieves a Precision of 0.837, Recall of 0.738, and mAP of 79, outperforming Faster R-CNN, RT-DETR, and YOLOv11, particularly for small-scale defects, confirming that ESS-DETR effectively balances accuracy, efficiency, and onboard deployability, providing a practical solution for intelligent UAV-based surface inspection.