Skip-cycleGAN:A Heterologous Image Registration Model for Orchard Apple
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.
image registrationheterologous imagesgenerative adversarial networkskip connectionridge regression loss
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