Aug 2026· 2026 IEEE International Conference on Mechatronics and Automation (ICMA)· pp. 1222-1227· 0 citations· 16 references
Abstract
In wind turbine blade inspection images, defects such as cracks, dirt and peeling often have small scales, weak boundaries, low contrast and obvious background texture interference. The original YOLOv11 is still unstable in recognizing such weak-feature defects. In this paper, a UFOAttention module is introduced after the initial convolution layer of YOLOv11 to construct a UFOAttention-YOLOv11 wind turbine blade surface defect detection model, and shallow spatial correlation enhancement is used to improve the feature representation of defect regions. Experiments are carried out on a dataset containing seven types of blade defects. The results show that UFOAttention-YOLOv11 achieves an mAP@0.5 of 0.895, which is 0.017 higher than the original YOLOv11, and an mAP@0.5:0.95 of 0.682, which is 0.057 higher. The model improves recognition accuracy for multiple defect categories, including crack, deformity, dirt and peeling, indicating that the method can effectively enhance the representation of weak-boundary and irregular defects in complex blade inspection scenarios.
First, MS-CBAM is introduced before the SPPF module to enhance channel-wise feature refinement and multi-scale spatial feature extraction and EPConv is replaced with EPConv, which combines efficient multi-scale channel attention with directional pinwheel-shaped convolution to strengthen the representation of weak and e...
Automated bridge crack detection is challenging because cracks often exhibit weak contrast, irregular morphology, slender structures, and strong interference from complex surface textures. To address these issues, this study proposes a YOLOv8-CA-EYHL framework that combines filtering-equalization preprocessing with coo...
Xian-Wei Zhu, He Chao, Ya-Hui Zhang· PLoS ONE· 0 citations
Addressing challenges in UAV power line inspection—where insulator defect detection models are prone to environmental interference, insufficient feature representation, and difficulty balancing lightweight requirements—this study develops a lightweight image defect detection model that integrates high accuracy with str...
In UAV aerial photography-based pavement defect detection, several challenges remain, including large variations in defect scales, complex background interference, difficult extraction of effective features, and the inability of existing methods to balance detection accuracy and real-time performance. To address these...
Yan Wu, Yi-Ming Li· International Conference on...· 0 citations
To address the limitations of existing visual detection algorithms for mining wire ropes (MWR), including low detection accuracy, limited capability to recognize tiny defects, and high computational cost, we first employ NAFNet to mitigate motion blur in the input images. We then propose DFD-YOLO, an improved YOLOv11-b...
The lightweight model reduces model complexity but also exhibits a non-negligible decrease in detection accuracy, demonstrating an explicit accuracy-complexity trade-off rather than accuracy-preserving compression.
Q. Peng, P. Zhong, C.-R. Yang· Advanced Electromagnetics· 0 citations
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