| Citation: | SU Zhiwei, HUANG Zihan, QIU Fasheng, et al. Weld defect detection of Aviation Aluminum alloy based on improved YOLOv8[J]. Journal of Aerospace Power, 2024, 39（6）：20230414 doi: 10.13224/j.cnki.jasp.20230414
|
In order to improve the efficiency and accuracy of automatic detection, this work proposed an improved YOLOv8 detection method. Firstly, Retinex image enhancement algorithm combing guided filtering was used to improve the contrast of digital radiograph images. Then, the digital radiography images was rotated and flipped to extend the data-set. In the process of model improvement, the Bottleneck module in C2f was replaced by GhostBottleneck module to reduce additional redundant parameters, and the model was light-weighed. In addition, spatial attention mechanism was introduced to obtain more spatial information of the defect. The regression range of the prediction box was adjusted to improve the detection accuracy of the proposed model. Finally, several common aluminum alloy weld defects were used for experimental testing and verification. It was verified that the mAP of the improved YOLOv8 was 92.9%, which was better than Faster-RCNN, SSD and YOLOv8. The proposed model enabled to detecting the weld defect.
| [1] |
臧金鑫,陈军洲,韩凯,等. 航空铝合金研究进展与发展趋势[J]. 中国材料进展,2022,41(10): 769-777,807. ZANG Jinxin,CHEN Junzhou,HAN Kai,et al. Research progress and development tendency of aeronautical aluminum alloys[J]. Materials China,2022,41(10): 769-777,807. (in Chinese
|
| [2] |
杜亮,闫福旭. 铝合金焊接常见缺陷及预防措施研究[J]. 中国金属通报,2021(7): 71-72. DU Liang,YAN Fuxu. Study on common defects in aluminum alloy welding and preventive measures[J]. China Metal Bulletin,2021(7): 71-72. (in Chinese
|
| [3] |
胡文刚,陆云鹏,郭世雄,等. 基于DR数字射线成像技术的铝合金焊缝缺陷检测[J]. 焊接,2021(2): 46-51,64. HU Wengang,LU Yunpeng,GUO Shixiong,et al. Weld defect detection of aluminum alloy based on digital radiography[J]. Welding & Joining,2021(2): 46-51,64. (in Chinese
|
| [4] |
邬冠华,熊鸿建. 中国射线检测技术现状及研究进展[J]. 仪器仪表学报,2016,37(8): 1683-1695. WU Guanhua,XIONG Hongjian. Radiography testing in China[J]. Chinese Journal of Scientific Instrument,2016,37(8): 1683-1695. (in Chinese
|
| [5] |
冯雄博,陈曦,闵慧娜,等. 基于改进CLAHE的航空发动机导向叶片DR图像增强[J]. 航空动力学报,2022,37(7): 1425-1436. FENG Xiongbo,CHEN Xi,MIN Huina,et al. DR image enhancement of aero-engine guide vane based on improved CLAHE[J]. Journal of Aerospace Power,2022,37(7): 1425-1436. (in Chinese
|
| [6] |
丁卫良,常华峰,潘龙龙,等. X射线无损检测的应用及发展趋势[J]. 科技创新与应用,2020(36): 161-162. DING Weiliang,CHANG Huafeng,PAN Longlong,et al. Application and development trend of X-ray nondestructive testing[J]. Technology Innovation and Application,2020(36): 161-162. (in Chinese
|
| [7] |
王克广,岳乔,缪伟,等. 发动机小直径管X射线底片数字化图像增强技术[J]. 失效分析与预防,2022,17(2): 86-95. WANG Keguang,YUE Qiao,MIAO Wei,et al. Digital image enhancement on X-ray film of aeroengine small diameter tube[J]. Failure Analysis and Prevention,2022,17(2): 86-95. (in Chinese
|
| [8] |
王睿,胡云雷,刘卫朋,等. 基于边缘AI的焊缝X射线图像缺陷检测[J]. 焊接学报,2022,43(1): 79-84,118. WANG Rui,HU Yunlei,LIU Weipeng,et al. Defect detection of weld X-ray image based on edge AI[J]. Transactions of the China Welding Institution,2022,43(1): 79-84,118. (in Chinese
|
| [9] |
GAO Jinling,FEZZAA K,CHEN Weinong. Multiscale dynamic experiments on fiber-reinforced composites with damage assessment using high-speed synchrotron X-ray phase-contrast imaging[J]. NDT & E International,2022,129: 102636.
|
| [10] |
GIRSHICK R,DONAHUE J,DARRELL T,et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway,US: IEEE,2014: 580-587.
|
| [11] |
ZHAO Wenqing,XU Minfu,CHENG Xingfu,et al. An insulator in transmission lines recognition and fault detection model based on improved faster RCNN[J]. IEEE Transactions on Instrumentation and Measurement,2021,70: 1-8.
|
| [12] |
蔡彪,沈宽,付金磊,等. 基于Mask R-CNN的铸件X射线DR图像缺陷检测研究[J]. 仪器仪表学报,2020,41(3): 61-69. CAI Biao,SHEN Kuan,FU Jinlei,et al. Research on defect detection of X-ray DR images of casting based on Mask R-CNN[J]. Chinese Journal of Scientific Instrument,2020,41(3): 61-69. (in Chinese
|
| [13] |
陈乐. 基于深度学习的小径管环焊缝DR图像缺陷智能识别[J]. 化工装备技术,2022,43(4): 30-35. CHEN Le. Intelligent recognition of small diameter pipe girth weld DR image defects based on deep learning[J]. Chemical Equipment Technology,2022,43(4): 30-35. (in Chinese
|
| [14] |
LIU Wei,ANGUERLOV D,ERHAN D E,et. al,SSD: Single shot multibox detector [C]// Proceedings of Computer Vision–ECCV 2016: 14th European Conference. Amsterdam: Springer,2016: 21-37.
|
| [15] |
李兰,奚舒舒,张才宝,等. 基于改进SSD模型的工件表面缺陷识别算法[J]. 计算机工程与科学,2020,42(9): 1608-1615. LI Lan,XI Shushu,ZHANG Caibao,et al. A surface defect recognition algorithm based on improved SSD model[J]. Computer Engineering & Science,2020,42(9): 1608-1615. (in Chinese
|
| [16] |
SHAFI I,MAZAHIR A,FATIMA A,et al. Internal defects detection and classification in hollow cylindrical surfaces using single shot detection and MobileNet[J]. Measurement,2022,202: 111836. doi: 10.1016/j.measurement.2022.111836
|
| [17] |
REDMON J,DIVVALA S,GIRSHICK R,et al. You only look once: unified,real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway,US: IEEE,2016: 779-788.
|
| [18] |
ZHANG Rui,WEN Chuanbo. SOD-YOLO: a small target defect detection algorithm for wind turbine blades based on improved YOLOv5[J]. Advanced Theory and Simulations,2022,5(7): 2100631. doi: 10.1002/adts.202100631
|
| [19] |
程松,杨洪刚,徐学谦,等. 基于YOLOv5的改进轻量型X射线铝合金焊缝缺陷检测算法[J]. 中国激光,2022,49(21): 136-144. CHENG Song,YANG Honggang,XU Xueqian,et al. Improved lightweight X-ray aluminum alloy weld defects detection algorithm based on YOLOv5[J]. Chinese Journal of Lasers,2022,49(21): 136-144. (in Chinese
|
| [20] |
赵先圣,冯鹏,沈宽,等. 基于深度学习的铁道车辆铸件X射线DR图像缺陷检测算法研究[J]. 中国体视学与图像分析,2021,26(3): 310-320. ZHAO Xiansheng,FENG Peng,SHEN Kuan,et al. Research on a defect detection algorithm for X-ray DR images of railway vehicle castings based on deep learning[J]. Chinese Journal of Stereology and Image Analysis,2021,26(3): 310-320. (in Chinese
|
| [21] |
LIU Moyun,CHEN Youping,XIE Jingming,et al. LF-YOLO: a lighter and faster YOLO for weld defect detection of X-ray image[J]. IEEE Sensors Journal,2023,23(7): 7430-7439. doi: 10.1109/JSEN.2023.3247006
|
| [22] |
RUIZ-PONCE P,ORTIZ-PEREZ D,GARCIA-RODRIGUEZ J,et al. POSEIDON: a data augmentation tool for small object detection datasets in maritime environments[J]. Sensors,2023,23(7): 3691. doi: 10.3390/s23073691
|
| [23] |
周涛,杜玉虎,石道宗,等. 强化特征提取能力的下颌骨骨折检测3M-YOLOv5网络[J]. 光学 精密工程,2023,31(21): 3178-3191. ZHOU Tao,DU Yuhu,SHI Daozong,et al. Mandibular fracture detection with 3M-YOLOv5 network based on enhanced feature extraction capability[J]. Optics and Precision Engineering,2023,31(21): 3178-3191. (in Chinese doi: 10.37188/OPE.20233121.3178
|
| [24] |
夏烨,雷哓晖,祁雁楠,等. 基于改进Ghost-YOLOv5s-BiFPN算法检测梨树花序[J]. 智慧农业(中英文),2022,4(3): 108-119. XIA Ye,LEI Xiaohui,QI Yannan,et al. Detection of pear inflorescence based on improved ghost-YOLOv5s-BiFPN algorithm[J]. Smart Agriculture,2022,4(3): 108-119. (in Chinese
|
DownLoad:
