DCEI-RTDETR: an improved RT-DETR-based detection algorithm for data center equipment indicator lights - Xu, Liangjie; Zeng, Wenxian (Journal of real-time image processing, 2025)
To address the issue of low detection accuracy caused by small target sizes and complex image backgrounds in the detection of indicator lights in data center equipment rooms, a lightweight small-target detection algorithm based on the Transformer architecture, named DCEI-RTDETR, is proposed. First, EfficientFormerV2 is utilized as the feature extraction network. By reducing the number of downsampling operations, the size of the feature maps is increased, enabling the network to focus more effectively on small targets. Subsequently, a high-level screening feature aggregation network is designed, which employs the HiLo attention mechanism at the highest feature level as an intra-feature interaction module. Simultaneously, the GSConv and VoVGCSCSP modules are used for cross-scale feature fusion, dynamically optimizing the feature map’s representation capability. Additionally, a one-to-many label assignment method is introduced, utilizing a grouped decoder to optimize object query processing, thereby alleviating the issues of occlusion and the loss of small target feature information. Finally, the GIOU loss function is replaced with Inner-EIOU incorporating a scaling factor to control the auxiliary bounding box, which improves the accuracy of small target detection. Experimental results on a proprietary data center equipment status detection dataset show that, compared to the original RT-DETR algorithm, the mAP50 is increased by 4.2%, the mAP50:95 by 2.1%, and the FPS is maintained at 90. Generalization experiments on the public VisDrone2021 dataset also demonstrate the effectiveness and generality of the proposed algorithm, with the mAP50 improved by 4.4%.