## 4 Results and Discussion
### Comparison of Computational Costs for Different Models  
To compare and analyze the computational costs of the four models, RT-DETR, RT-DETR-Tomato-B, RT-DETR-Tomato-S, and RT-DETR-Tomato-BS, we can consider multiple aspects, including the model training time, resource consumption, and detection accuracy. The following is a detailed cost comparison analysis of each model:  
1. Model Training Time The training times for RT-DETR, RT-DETR-Tomato-B, RT-DETR-Tomato-S, and RT-DETR-Tomato-BS models are 2.057 h, 2.633 h, 1.091 h, and 1.305 h, respectively. The RT-DETR-Tomato-S model uses the Swin Transformer to replace the original ResNet50 backbone of the RE-DETR model; the RT-DETR-Tomato-B model introduces the Bi-Former Vision Transformer with dual-level routing attention to enhance small object feature extraction capability. The RT-DETR-Tomato-BS model replaces the original ResNet50 backbone of the RE-DETR model with the Swin Transformer and simultaneously introduces the BiFormer Vision Transformer to enhance small object feature extraction capability. Shorter training times typically indicate lower computational resource demands and, thus, lower costs. Therefore, it can be seen that the RT-DETR-Tomato-B model has the lowest training time cost, while the RT-DETR-Tomato-S model has the highest. 2. Detection Performance The maximum mAP values for the RT-DETR, RT-DETR-Tomato-B, RT-DETR-Tomato-S, and RT-DETR-Tomato-BS models are 85.4%, 87.4%, 88.0%, and 88.7%, respectively. The RT-DETR-Tomato-S model uses the Swin Transformer to replace the original ResNet50 backbone of the RE-DETR model; the RT-DETR-Tomato-B model introduces the BiFormer Vision Transformer with dual-level routing attention to enhance small object feature extraction capability. The RT-DETR-Tomato-BS model replaces the original ResNet50 backbone of the RE-DETR model with the Swin Transformer and simultaneously introduces the BiFormer Vision Transformer to enhance small object feature extraction capability. The RT-DETR-Tomato-BS model shows the best detection performance, while the RT-DETR model shows the worst. 3. Resource Consumption and Complexity RT-DETR-Tomato-S model: This model replaces the original ResNet50 backbone of the RE-DETR model with the Swin Transformer. The hierarchical Transformer structure captures richer global dependencies and contextual information, enabling stronger feature representation in complex images and scenes, and allowing processing features at different resolutions, thereby improving detection accuracy. However,  
Figure 12: Comparison of the precision curves for each model. the Swin Transformer blocks in the backbone network have redundant parameters, increasing resource consumption and computational complexity compared to the RT-DETR model. RT-DETR-Tomato-B model: This model introduces the BiFormer Vision Transformer with dual-level routing attention to enhance small object feature extraction capability. BiFormer technology achieves more flexible content-aware computation allocation using lightweight BiFormer blocks. The model effectively reduces computational complexity through flexible computation allocation in the BiFormer block, increasing resource consumption but reducing computational complexity compared to the RT-DETR model. RT-DETR-Tomato-B model: This model replaces the original ResNet50 backbone of the RE-DETR model with the Swin Transformer and simultaneously introduces the BiFormer Vision Transformer. This improves feature extraction capability while reducing computational complexity but increases resource consumption. 4. Overall Cost Assessment The RT-DETR model has moderate training time, the lowest performance, and moderate cost. The RT-DETR-Tomato-B model has the shortest training time, good performance, and the lowest cost. The RT-DETR-Tomato-S model has the longest training time, good performance, and the highest cost. The RT-DETR-Tomato-BS model has a relatively short training time, the best performance, and the best overall cost.