## 1 Introduction  
With the advancement of Industry 4.0, machine-vision technology has replaced traditional manual inspection methods, enabling real-time monitoring of the stamping process to protect models [1]. By capturing images of the metal sheet before and after the mold closes, the process is analyzed for any abnormalities. If an anomaly is detected, an emergency stop signal is issued to halt the stamping equipment immediately, thereby protecting the molds, reducing economic losses, and enhancing production efficiency. However, the actual stamping process may be affected by factors such as the impact force of the mold opening and closing or unstable feed rates from the feeder, causing random vibrations in the metal sheets. This results in random offsets in the images captured each time, necessitating image registration of the template and real-time detection images. The difference between these two images is then used to determine if there are any abnormalities in the process. Therefore, image registration algorithms are indispensable in the application of monitoring the stamping process. Image registration refers to the process of transforming different images of the same object from different spatial coordinate systems to the same coordinate system through a certain mapping relationship. In this process, homography estimation plays an important role. Traditional homography estimation methods are divided into direct methods and feature-based matching methods [2]. Direct methods like the Lucas-Kanade algorithm initialize homography parameters and use gradient descent to minimize errors [3; 4]. Other methods include optical flow and image gradient-based techniques [5; 6], which are sensitive to lighting changes and require good initial values to prevent local optima. The optimization is computationally intensive, complicating their use in high-resolution and complex scenes. The second method involves feature-based matching, which starts with feature detection algorithms like SIFT, SURF, and ORB to extract key points from images [7; 8; 9]. Each detected key point is assigned a descriptor that characterizes its surrounding image area. These feature descriptors are then compared between two images using matchers like FLANN or Brute-Force to find matching pairs [10]. Finally, robust estimation methods such as RANSAC or LMedS are used to compute the optimal homography matrix [11; 12]. While these methods generally perform faster than direct methods, they heavily rely on the detection and matching of key points. In areas with low texture, sufficient key points may not be detectable, and significant changes in perspective or lighting between images can lead to incorrect key point matches, directly impacting the accuracy of homography estimation. The stamping surveillance environment is influenced by external factors like lighting changes and equipment instability, as well as human factors such as camera angle adjustments. These factors can significantly alter the comparison between real-time detection and template images, impacting the accuracy of feature-point detection and homography estimation. Leveraging the success of deep convolutional neural networks in computer vision, deep-learning-based homography estimation methods can effectively tackle these robustness issues and the reliance on feature points seen in traditional approaches [13]. [PERSON] et al. introduced HomographyNet, a simple convolutional neural network trained end-to-end to directly regress the parameters of the homography matrix [14]. It outperforms traditional methods in complex scenarios such as low-texture environments, but the model is trained on synthetic datasets, leading to poor inference capabilities on real images. Real-world image labeling is also highly costly. [PERSON] et al. [15] were the first to propose an unsupervised homography estimation method.