Version 1
: Received: 6 September 2024 / Approved: 6 September 2024 / Online: 6 September 2024 (16:52:09 CEST)
How to cite:
Yang, S.; Liu, Y.; Liu, Z.; Xu, C.; Du, X. Enhanced Vehicle Logo Detection Method Using Mamba Structure for Electric Vehicle Application. Preprints2024, 2024090558. https://doi.org/10.20944/preprints202409.0558.v1
Yang, S.; Liu, Y.; Liu, Z.; Xu, C.; Du, X. Enhanced Vehicle Logo Detection Method Using Mamba Structure for Electric Vehicle Application. Preprints 2024, 2024090558. https://doi.org/10.20944/preprints202409.0558.v1
Yang, S.; Liu, Y.; Liu, Z.; Xu, C.; Du, X. Enhanced Vehicle Logo Detection Method Using Mamba Structure for Electric Vehicle Application. Preprints2024, 2024090558. https://doi.org/10.20944/preprints202409.0558.v1
APA Style
Yang, S., Liu, Y., Liu, Z., Xu, C., & Du, X. (2024). Enhanced Vehicle Logo Detection Method Using Mamba Structure for Electric Vehicle Application. Preprints. https://doi.org/10.20944/preprints202409.0558.v1
Chicago/Turabian Style
Yang, S., Changhua Xu and Xueting Du. 2024 "Enhanced Vehicle Logo Detection Method Using Mamba Structure for Electric Vehicle Application" Preprints. https://doi.org/10.20944/preprints202409.0558.v1
Abstract
Vehicle logo detection plays a crucial role in various computer vision applications, such as vehicle classification and detection. In this research, we propose an improved vehicle logo detection method leveraging the MAMBA structure. The MAMBA structure integrates multiple attention mechanisms and bidirectional feature aggregation to enhance the discriminative power of the detection model. Specifically, we introduce the Multi-Head Attention for Multi-Scale Feature Fusion (MHAMFF) module to capture multi-scale contextual information effectively. Moreover, we incorporate the Bidirectional Aggregation Mechanism (BAM) to facilitate information exchange between different layers of the detection network. Experimental results on a benchmark dataset (VLD-45 dataset) demonstrate that our proposed method outperforms baseline models in terms of both detection accuracy and efficiency. Furthermore, extensive ablation studies validate the effectiveness of each component in the MAMBA structure. Overall, the proposed MAMBA-based vehicle logo detection approach shows promising potential for real-world applications in intelligent transportation systems.
Keywords
Vehicle logo detection; Mamba; Multi-Head Attention; Multi-Scale Feature Fusion
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
