Real-time Asset False Matching Detection via Attentive Siamese Networks

Accurate and efficient asset matching is crucial for remote management and diversified investment entities. To address the challenges of low efficiency, high labor costs, and false matching, this paper proposes a real-time asset false matching detection method based on an attentive Siamese network. By integrating the Convolutional Block Attention Module (CBAM) into a Siamese neural network framework, the model enhances feature extraction capabilities, focusing on discriminative regions within asset images. Pretrained weights are transferred using transfer learning, and data augmentation techniques are applied to enhance the model's generalization ability. Experiments on various types of asset data demonstrate that the proposed method achieves an accuracy of 92.42% and an F1 score of 92.48%, significantly improving the precision and efficiency of asset similarity calculations. This research not only advances asset identification and matching but also showcases exceptional performance in intelligent asset inventory, providing reliable support for asset management. Future work will focus on improving model robustness and optimizing the network structure for large-scale and complex environments.