计算机技术与发展2024，Vol.34Issue(7)：40-47,8.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0104
Skip-cycleGAN:一种果园苹果异源图像配准模型
Skip-cycleGAN:A Heterologous Image Registration Model for Orchard Apple
摘要
Abstract
Aiming at the problems that the performance of the supervised registration model is limited by the given labels as well as the unstable training of the loop consistency generative adversarial network,which has a slow convergence speed,is prone to overfitting,and is ineffective in image processing for complex scenes,an unsupervised heterologous image alignment model is proposed based on the im-provement of loop consistency generative adversarial network from the three aspects of the generator,the discriminator,and the loss function.The introduction of a jump connection with a feature transformation residual layer between the downsampling and upsampling of the generative network ensures the effective transfer of gradients,reduces the loss of information in the process of forward and backward propagation,and achieves the combination of low-level features and high-level features,thus alleviating the gradient vanishing and the gradient explosion,promoting the convergence of the neural network,and helping the network to learn more contextual information.The model is evaluated on a self-built orchard apple dataset and two public datasets,and the experiment concludes that on the basis of the improved generator,it is more appropriate to select the 70×70 PatchGAN discriminator for datasets with relatively large deformation,and the PixelGAN discriminator for datasets with relatively small deformation.Comparing with eight classical algorithms and evaluating with six performance metrics,the experimental results show that the comprehensive performance of the proposed model on the heterologous orchard apple dataset is better than that of the comparison algorithms.Future work will be done to improve the robustness of the model to the brightness and contrast of heterologous images and to lighten the model.关键词
图像配准/异源图像/生成对抗网络/跳跃连接/岭回归损失Key words
image registration/heterologous images/generative adversarial network/skip connection/ridge regression loss分类
信息技术与安全科学引用本文复制引用
何亚鹏,刘立群..Skip-cycleGAN:一种果园苹果异源图像配准模型[J].计算机技术与发展,2024,34(7):40-47,8.基金项目
甘肃省高校教师创新基金项目(2023A-051) （2023A-051）
甘肃农业大学青年导师基金资助项目(GAU-QDFC-2020-08) （GAU-QDFC-2020-08）
甘肃省科技计划资助(20JR5RA032) （20JR5RA032）
