| 摘要: | 從車輛中的行車紀錄器或是從監視器拍攝到的車牌影像，可能會因為拍攝時距離過遠、沒有對焦、車速太快等因素，導致影像變得模糊，使得一般的車牌辨識系統無法精確辨識車牌。雖然已有不少文獻使用生成對抗網路(GAN)去實踐，並有相關的研究成果，但成效不一，有些重建成功率較低、有些只對於特定的模糊方式解模糊較有效。在研究不同文獻後，發現不同的GAN架構，其重建影像的結果就有很明顯的差異。因此本論文將尋找並修改現有的GAN架構，將不同的生成器架構、判別器架構以及損失函數做配對與組合，並比較不同組合下，其重建模糊影像的效果優劣，以找出效果最好的重建效果的組合。另外，我們也對增加影像重建次數是否會使重建效果變好，感到好奇，並給予實驗與分析。
我們把重建成功的標準分為兩種，一為”車牌完全重建正確”，二為”重建出來可以辨識”。其中第一種為對於人眼能勉強判讀模糊影像中的車牌號碼，重建後影像中的車牌完全正確；第二種為車牌模糊程度高至人眼無法判讀，重建後車牌影像可以辨識以作為參考(不見得重建完全正確)。兩種的評估重建成功的指標均使用SSIM (structural similarity)。本論文最後的結果顯示，不論採用哪一種標準，使用DeblurGAN的重建效果最好，其中生成器使用含有全局跳躍連接的ResNet，判別器使用多尺度的PatchGAN，在不分類的情況下，整體avgSSIM為0.8036。關於影像重建次數的部分，如果為第一種車牌，其重建1次的車牌影像的avgSSIM已經達到0.8536，代表重建1次的車牌影像已經夠清楚且完全正確，不需要進行二次重建，重建2次的avgSSIM因為背景或少許區塊與原始影像更不同，因此下降成0.7647。如果為第二種車牌，重建出來的車牌時常不夠清晰或是不完全正確，如果重建出的車牌不夠清晰，可以將車牌進行多次重建，直到車牌可以辨識以作為參考或是無法變更清楚為止。即使重建1次的avgSSIM比重建2次的高，但重建後的車牌能夠辨識比正確更為重要。因此第二種車牌得根據重建出的車牌的情況來決定最佳的影像重建次數。
;The license plate image captured by the dashcam in the vehicle or the monitor may blur due to the distance, lack of focus, or high speed of the vehicle. Therefore, the license plate recognition system can’t accurately identify the license plate. Although there is a lot of literature that uses Generative Adversarial Networks (GAN) to achieve and have related research results, the results are different. Some reconstruction success rates are low, and some are only effective for specific blurring methods. After studying different literature, it’s found that different GAN architectures have obvious differences in the results of reconstructed images. Therefore, this thesis searches for and modifies the existing GAN architecture, pairs and combines different generator architectures, discriminator architectures, and loss functions, and compares the effects of reconstructed images under different combinations to find the combination with the best reconstruction effect. In addition, we are also curious about whether increasing the number of image reconstructions will improve the reconstruction effect, and give experiments and analysis.
We divide the reconstruction success criteria into two types, one is "the license plate is completely reconstructed correctly", and the other is "the license plate can be recognized after reconstruction". The first type is that the human eye can barely read the license plate number in the blurred image, and the license plate number in the reconstructed image is completely correct. The second type is that the license plate is so blurred that it can not be read by the human eye, and the reconstructed license plate image can be recognized as a reference (the reconstruction may not be completely correct). Both metrics for evaluating the success of reconstruction use SSIM (structural similarity). The final results of this thesis show that no matter which standard is used, the reconstruction effect using DeblurGAN is the best, where the generator uses ResNet with global skip connections, and the discriminator uses multi-scale PatchGAN. Without classification, the overall avgSSIM is 0.8036. Regarding the number of image reconstruction times, if it is the first type of license plate, the avgSSIM of the license plate image reconstructed once has reached 0.8536, which means that the license plate image reconstructed once is clear enough and completely correct, and there is no need for secondary reconstruction. The avgSSIM of reconstruction twice drops to 0.7647 because the background or a few blocks are more different from the original image. Generally, if it is the second type of license plate, the reconstructed license plate is still blurred or not completely correct. If the reconstructed license plate is not clear enough, the license plate can be reconstructed several times until the license plate can be recognized as a reference or can not be changed clearly. Even though the avgSSIM of reconstruction once is higher than that of reconstruction twice, it is more important to be able to recognize the reconstructed license plate than to be correct. Therefore, the second type of license plate has to determine the optimal number of image reconstructions according to the condition of the reconstructed license plate. |
