LFTA:Lightweight Feature Extraction and Additive Attention-based Feature Matching Method

GUO Zhiqiang; WANG Zihan; WANG Yongsheng; CHEN Pengyu

doi:10.11999/JEIT250124

Volume 47 Issue 8

Aug. 2025

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GUO Zhiqiang, WANG Zihan, WANG Yongsheng, CHEN Pengyu. LFTA:Lightweight Feature Extraction and Additive Attention-based Feature Matching Method[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2872-2882. doi: 10.11999/JEIT250124

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GUO Zhiqiang, WANG Zihan, WANG Yongsheng, CHEN Pengyu. LFTA:Lightweight Feature Extraction and Additive Attention-based Feature Matching Method[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2872-2882. doi: 10.11999/JEIT250124

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LFTA:Lightweight Feature Extraction and Additive Attention-based Feature Matching Method

doi: 10.11999/JEIT250124 cstr: 32379.14.JEIT250124

1.
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
2.
School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China

Funds: Taiyuan City’s “Double Hundred Key Technology Breakthrough Initiative” (2014TYJB0126)

Received Date: 2025-03-03
Rev Recd Date: 2025-07-01

Available Online: 2025-07-08

Publish Date: 2025-08-27

Abstract

Abstract

Objective With the rapid development of deep learning, feature matching has advanced considerably, particularly in computer vision. This progress has led to improved performance in tasks such as 3D reconstruction, motion tracking, and image registration, all of which depend heavily on accurate feature matching. Nevertheless, current techniques often face a trade-off between accuracy and computational efficiency. Some methods achieve high matching accuracy and robustness but suffer from slow processing due to algorithmic complexity. Others offer faster processing but compromise matching accuracy, especially under challenging conditions such as dynamic scenes, low-texture environments, or large view-angle variations. The key challenge is to provide a balanced solution that ensures both accuracy and efficiency. To address this, this paper proposes a Lightweight Feature exTraction and matching Algorithm (LFTA), which integrates an additive attention mechanism within a lightweight architecture. LFTA enhances the robustness and accuracy of feature matching while maintaining the computational efficiency required for real-time applications. Methods LFTA utilizes a multi-scale feature extraction network designed to capture information from images at different levels of detail. A triple-exchange fusion attention mechanism merges information across multiple dimensions, including spatial and channel features, allowing the network to learn more robust feature representations. This mechanism improves matching accuracy, particularly in scenarios with sparse textures or large viewpoint variations. LFTA further integrates an adaptive Gaussian kernel to dynamically generate keypoint heatmaps. The kernel adjusts according to local feature strength, enabling accurate keypoint extraction in both high-response and low-response regions. To improve keypoint precision, a dynamic Non-Maximum Suppression (NMS) strategy is applied, which adapts to varying keypoint densities across different image regions. This approach reduces redundancy and improves detection accuracy. In the final stage, LFTA employs a lightweight module with an additive Transformer attention mechanism to refine feature matching. This module strengthens feature fusion while reducing computational complexity through depthwise separable convolutions. These operations substantially lower parameter count and computational cost without affecting performance. Through this combination of techniques, LFTA achieves accurate pixel-level matching with fast inference times, making it suitable for real-time applications. Results and Discussions The performance of LFTA is assessed through extensive experiments conducted on two widely used and challenging datasets: MegaDepth and ScanNet. These datasets offer diverse scenarios for evaluating the robustness and efficiency of feature matching methods, including variations in texture, environmental complexity, and viewpoint changes. The results indicate that LFTA achieves higher accuracy and computational efficiency than conventional feature matching approaches. On the MegaDepth dataset, an AUC@20° of 79.77% is attained, which is comparable to or exceeds state-of-the-art methods such as LoFTR. Notably, this level of performance is achieved while reducing inference time by approximately 70%, supporting the suitability of LFTA for practical, time-sensitive applications. When compared with other efficient methods, including Xfeat and Alike, LFTA demonstrates superior matching accuracy with only a marginal increase in inference time, proving its competitive performance in both accuracy and speed. The improvement in accuracy is particularly apparent in scenarios characterized by sparse textures or large viewpoint variations, where traditional methods often fail to maintain robustness. Ablation studies confirm the contribution of each LFTA component. Exclusion of the triple-exchange fusion attention mechanism results in a significant reduction in accuracy, indicating its function in managing complex feature interactions. Similarly, both the adaptive Gaussian kernel and dynamic NMS are found to improve keypoint extraction, emphasizing their roles in enhancing overall matching precision. Conclusions The LFTA algorithm addresses the long-standing trade-off between feature extraction accuracy and computational efficiency in feature matching. By integrating the triple-exchange fusion attention mechanism, adaptive Gaussian kernels, and lightweight fine-tuning strategies, LFTA achieves high matching accuracy in dynamic and complex environments while maintaining low computational requirements. Experimental results on the MegaDepth and ScanNet datasets demonstrate that LFTA performs well under typical feature matching conditions and shows clear advantages in more challenging scenarios, including low-texture regions and large viewpoint variations. Given its efficiency and robustness, LFTA is well suited for real-time applications such as Augmented Reality (AR), autonomous driving, and robotic vision, where fast and accurate feature matching is essential. Future work will focus on further optimizing the algorithm for high-resolution images and more complex scenes, with the potential integration of hardware acceleration to reduce computational overhead. The method could also be extended to other computer vision tasks, including image segmentation and object detection, where reliable feature matching is required.
- Feature matching,
- Additive attention mechanism,
- Lightweight network,
- Adaptive keypoint extraction,
- Pixel-level matching

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References(20)

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