To address the poor performance of existing search-and-rescue detection methods in complex environments, this study proposes an enhanced algorithm named DAAS-RT-DETR. Based on the real-time detection transformer (RT-DETR) framework, the method first applies dual convolution kernels (DualConv) to strengthen multi-scale feature extraction. The global attention mechanism (GAM) is then incorporated into the attention-based intrascale feature interaction (AIFI) module to jointly capture spatial and channel dependencies. An adaptive gated fusion module (AGFM) is further introduced to improve feature interaction efficiency, and the loss function is replaced with scale-based dynamic loss (SBD Loss) to achieve more accurate object localization.Experimental results show that the proposed algorithm improves mAP50 and mAP50-95 by 4.6% and 4.2% on the AiTV dataset, and by 6.3% and 6.2% on the self-built RES dataset, respectively, while requiring fewer parameters. These gains highlight the strong practical potential of the method for real-world search-and-rescue missions.