A Step-Wise Domain Adaptation Detection Transformer for Object Detection under Poor Visibility Conditions

作者：Gege Zhang, Luping Wang, Zengping Chen · 发表于：Remote Sensing · 年份：2024 · DOI：10.3390/rs16152722 · 被引用次数：9 · 研究领域：Advanced Neural Network Applications、Domain Adaptation and Few-Shot Learning、Multimodal Machine Learning Applications

To address the performance degradation of cross-domain object detection under various illumination conditions and adverse weather scenarios, this paper introduces a novel method a called Step-wise Domain Adaptation DEtection TRansformer (SDA-DETR). Our approach decomposes the adaptation process into three sequential steps, progressively transferring knowledge from a labeled dataset to an unlabeled one using the DETR (DEtection TRansformer) architecture. Each step precisely reduces domain discrepancy, thereby facilitating effective transfer learning. In the initial step, a target-like domain is constructed as an auxiliary to the source domain to reduce the domain gap at the image level. Then, we adaptively align the source domain and target domain features at both global and local levels. To further mitigate model bias towards the source domain, we develop a token-masked autoencoder (t-MAE) to enhance target domain features at the semantic level. Comprehensive experiments demonstrate that the SDA-DETR outperforms several popular cross-domain object detection methods on three challenging public driving datasets.