Self-Aware Adaptive Alignment: Enabling Accurate Perception for Intelligent Transportation Systems

• URL: http://arxiv.org/abs/2508.13823v1
• Date: Tue, 19 Aug 2025 13:33:03 GMT
• Title: Self-Aware Adaptive Alignment: Enabling Accurate Perception for Intelligent Transportation Systems
• Authors: Tong Xiang, Hongxia Zhao, Fenghua Zhu, Yuanyuan Chen, Yisheng Lv,
• Abstract summary: Our proposed method employs a specified attention-based alignment module trained on source and target domain datasets.<n>Features from both domains, whose channel importance is re-weighted, are fed into the region proposal network.<n>Experiments show that SA3 achieves superior results to the previous state-of-the-art methods.
• Score: 19.841516107325898
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Achieving top-notch performance in Intelligent Transportation detection is a critical research area. However, many challenges still need to be addressed when it comes to detecting in a cross-domain scenario. In this paper, we propose a Self-Aware Adaptive Alignment (SA3), by leveraging an efficient alignment mechanism and recognition strategy. Our proposed method employs a specified attention-based alignment module trained on source and target domain datasets to guide the image-level features alignment process, enabling the local-global adaptive alignment between the source domain and target domain. Features from both domains, whose channel importance is re-weighted, are fed into the region proposal network, which facilitates the acquisition of salient region features. Also, we introduce an instance-to-image level alignment module specific to the target domain to adaptively mitigate the domain gap. To evaluate the proposed method, extensive experiments have been conducted on popular cross-domain object detection benchmarks. Experimental results show that SA3 achieves superior results to the previous state-of-the-art methods.

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