## 4 Experimental Evaluation
### Experimental Results and Analysis
#### 4.4.2 Comparison Experiment  
To evaluate the effectiveness of various state-of-the-art models in indoor human detection, we selected nine representative lightweight network models for comparison: YOLOv5s, YOLOv5 sp6, YOLOv6s, YOLOv8s, YOLOv9s, YOLOv10s, CenterNet [44], EfficientDet [45], and RT-DETR-L [46]. In order to maintain consistency in evaluation, all models were trained and tested using the HCIE dataset proposed in this paper. In addition to the four performance indicators used in Table 2, a new indicator called model size has been added. Table 3 summarizes the comparative results of the tests.  
The CIIHD-YOLO model, with 11.24 million parameters and 27.8 GFLOPs, strikes a balance between complexity and performance, achieving an impressive 86.76% mAP50 accuracy. It outperforms other models in precision, especially at IoU thresholds of 50-95, with a mAP of 64.19%, surpassing similar models. This means that the model can detect as many targets as possible, avoiding missed detections as much as possible, even if the targets are small or in complex backgrounds.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & **SPPEPSA** & **GSEAM** & **GSCConv** & **mAP50** & **mAP50-95** & **Params (M)** & **FLOPs (G)** \\ \hline YOLOv8s & \(\times\) & \(\times\) & \(\times\) & 85.77 & 61.52 & 11.17 & 28.8 \\ YOLOv8s\_1 & \(\surd\) & \(\times\) & \(\times\) & 86.15 & 62.57 & 12.16 & 29.6 \\ YOLOv8s\_2 & \(\times\) & \(\surd\) & \(\times\) & 86.23 & 63.05 & 10.17 & 26.9 \\ YOLOv8s\_3 & \(\times\) & \(\times\) & \(\surd\) & 86.14 & 63.07 & 11.25 & 28.9 \\ YOLOv8s\_4 & \(\surd\) & \(\surd\) & \(\times\) & 86.42 & 63.38 & 11.16 & 27.7 \\ YOLOv8s\_5 & \(\surd\) & \(\times\) & \(\surd\) & 86.39 & 63.50 & 12.24 & 29.7 \\ YOLOv8s\_6 & \(\times\) & \(\surd\) & \(\surd\) & 86.52 & 63.77 & 10.25 & 27.0 \\ CIIHD-YOLO & \(\surd\) & \(\surd\) & \(\surd\) & 86.76 & 64.19 & 11.24 & 27.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation experiments with different improvement strategies.  
Among various comparison algorithms, YOLOv6s has the closest detection accuracy to CHD-YOLO, but its computational requirements and model size are significantly higher. Although YOLOv9s and YOLOv10s models have faster computational performance on model size, params, and FLOPs, their mAP50s are 2.49% and 2.66% lower than that of CIID-YOLO, respectively, and their mAP50-95s are 3.3% and 2.73% lower, respectively, indicating a certain gap in accuracy. As a transformer-based model, RT-DETR-L has strong detection capabilities, but in the reproduction results of this study, its accuracy in detecting indoor human bodies under low-light conditions is poor. Although the transformer model performs well in handling long-distance dependencies, it may be difficult for the model to accurately capture subtle features and relationships between target objects in low-light environments. This may lead to the poor detection accuracy of RT-DETR in human detection tasks in low-light indoor environments.  
In summary, these results indicate that the enhanced CIID-YOLO model not only ensures excellent detection accuracy but also maintains a low computational load, significantly enhancing the network's capabilities.