High speed operation of trains can easily cause mechanical damage to surface components,affecting the safety of train operation.The trouble of moving electric multiple units detection system (TEDS) used for damage detection needs to detect components with diverse shapes and various volume sizes.Moreover,due to train movement and vibration of shooting equipment,the captured images may be blurred to varying degrees,which can interfere with the analyzing and labeling of faults by staff and affect the real-time and accuracy of detection.Therefore,a blind deblurring algorithm for TEDS images based on lightweight attention generative adversarial networks was proposed.Firstly,an improved linear inverse residual bottleneck module with channel attention and spatial attention mechanisms was used to construct a lightweight feature extraction network.Subsequently,the extracted features of five scales were fed into the Feature Pyramid Network (FPN) to construct a generator,enabling the network to effectively focus on key information,integrate low-level and high-level information,and extract features from multiple scales.Secondly,spectral normalization U_Net was used as the discriminator network to generate more accurate gradient feedback for local information.On the basis of local and global dual discriminators,pixel by pixel discrimination was added to enhance adversarial learning on image texture and details.Research results are shown as follows.The TEDS images processed by this algorithm have higher clarity compared to other algorithms,with evaluation indicators PSNR and SSIM reaching 28.6 and 91.2％,which are improved by 0.7 and 3.8％ compared to other algorithms.The lightweight network parameter file is only 13.6 M,which can achieve several tens of times the speed improvement of other algorithms,and can deblurring 75 TEDS images per minute without GPU,meeting the real-time requirements of the TEDS system.The results can effectively improve the image quality of the TEDS system,improve the accuracy of damage detection and labeling,enhance the efficiency of staff,and better ensure the safe operation of railways.
关键词
动车组运行故障图像检测系统/盲去模糊/注意力机制/生成对抗网络/MobileNet
Key words
trouble of moving electric multiple units detection system (TEDS)/blind deblurring/attention mechanism/generative adversarial network/MobileNet
维普
万方数据