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

TL;DR

This paper introduces SA3, a self-aware adaptive alignment method that enhances cross-domain object detection in intelligent transportation systems by aligning features at multiple levels using attention mechanisms.

Contribution

The paper presents a novel attention-based alignment module and an instance-to-image level adaptation strategy for improved cross-domain detection performance.

Findings

SA3 outperforms previous state-of-the-art methods on benchmark datasets.

The adaptive alignment effectively reduces domain gaps in detection tasks.

The method demonstrates robustness across various cross-domain scenarios.

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.