Detail texture detection based on Yolov4‐tiny combined with attention mechanism and bicubic interpolation
Aero‐engine blades crack detection is one of the important tasks in daily ground maintenance, crack is a kind of texture feature, due to the random distribution, irregular shape and vague characteristics, which is still a challenging task to realize automatic detection in working environment. A dete...
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| Published in | IET image processing Vol. 15; no. 12; pp. 2736 - 2748 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Wiley
01.10.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1751-9659 1751-9667 1751-9667 |
| DOI | 10.1049/ipr2.12228 |
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| Abstract | Aero‐engine blades crack detection is one of the important tasks in daily ground maintenance, crack is a kind of texture feature, due to the random distribution, irregular shape and vague characteristics, which is still a challenging task to realize automatic detection in working environment. A detection model based on the Yolov4‐tiny is proposed that is universal and focuses more on the characteristics of cracks, and it is implemented in embedded device. First, in order to distinguish the cracks and noises, an improved attention module is introduced into the backbone of Yolov4‐tiny to enhance the model's capability to focus on crack areas; second, in order to improve the effect of multi‐scale feature fusion, the bicubic interpolation is implemented in upsampling module; finally, in order to solve the redundant detection results of bounding‐boxes in crack areas, the optimized non‐maximum suppression method is proposed to make the detection results better corresponding to the groundTruth. The robustness of proposed detection model was demonstrated by evaluating varying lighting and noise images. The average precision on integrated datasets is 81.6%, which outperforms the original Yolov4‐tiny by an increase of 12.3%. |
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| AbstractList | Aero‐engine blades crack detection is one of the important tasks in daily ground maintenance, crack is a kind of texture feature, due to the random distribution, irregular shape and vague characteristics, which is still a challenging task to realize automatic detection in working environment. A detection model based on the Yolov4‐tiny is proposed that is universal and focuses more on the characteristics of cracks, and it is implemented in embedded device. First, in order to distinguish the cracks and noises, an improved attention module is introduced into the backbone of Yolov4‐tiny to enhance the model's capability to focus on crack areas; second, in order to improve the effect of multi‐scale feature fusion, the bicubic interpolation is implemented in upsampling module; finally, in order to solve the redundant detection results of bounding‐boxes in crack areas, the optimized non‐maximum suppression method is proposed to make the detection results better corresponding to the groundTruth. The robustness of proposed detection model was demonstrated by evaluating varying lighting and noise images. The average precision on integrated datasets is 81.6%, which outperforms the original Yolov4‐tiny by an increase of 12.3%. Abstract Aero‐engine blades crack detection is one of the important tasks in daily ground maintenance, crack is a kind of texture feature, due to the random distribution, irregular shape and vague characteristics, which is still a challenging task to realize automatic detection in working environment. A detection model based on the Yolov4‐tiny is proposed that is universal and focuses more on the characteristics of cracks, and it is implemented in embedded device. First, in order to distinguish the cracks and noises, an improved attention module is introduced into the backbone of Yolov4‐tiny to enhance the model's capability to focus on crack areas; second, in order to improve the effect of multi‐scale feature fusion, the bicubic interpolation is implemented in upsampling module; finally, in order to solve the redundant detection results of bounding‐boxes in crack areas, the optimized non‐maximum suppression method is proposed to make the detection results better corresponding to the groundTruth. The robustness of proposed detection model was demonstrated by evaluating varying lighting and noise images. The average precision on integrated datasets is 81.6%, which outperforms the original Yolov4‐tiny by an increase of 12.3%. |
| Author | Hui, Tian Jarhinbek, Rasol Xu, YueLei |
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| Title | Detail texture detection based on Yolov4‐tiny combined with attention mechanism and bicubic interpolation |
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