## 6 Experimental Results
### Comparisons
Compared with MobileNet, Fast R-CNN, YOLOv2 and YOLOv2 tiny, our solutions outperforms these models in both detection accuracy and efficiency. The last five solutions are based on the two latest YOLO versions. Among them, YOLOv5n has the best detection speed. Our result, H_Arch_C_8, can still achieve 3 times faster results with little loss of F1. To evaluate the detection accuracy a step further, we conducted experiments by setting different thresholds of confidence. In Figure 13, when the threshold of confidence increases, the F1 curves of YOLOv4 tiny and YOLOv5n decay more slowly than H_Arch_C_8 and YOLOv2. That is because YOLOv4 tiny and YOLOv5n have more outputs with greater confidence scores. Hence, fewer targets were lost when the bar of confidence increased. We then conducted comparisons with referenced studies [5,6,19,26], as they are lightweight models and provide experimental results on the same SSDD dataset as ours. Note that different GPUs are used in different studies. We only address fair and reasonable comparison results in Table 11 to demonstrate the detection efficiency and accuracy. In addition to S_Arch_C_8 and S_Arch_C_4, all data listed are directly extracted from references. Clearly, Darts_tiny can generate lighter and more efficient neural networks while maintaining a competitive detection accuracy (with only a few points lost in the F1 score). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Architectures** & **Parameters** & **Volume** & **FLOPs** & **Epochs** & **Time/Epoch** & **AP** & **F1** & **FPS** \\ \hline H\_Arch\_32 & 280 K & 1.10 MB & 6.89 G & 80 & 4 m & 85.09 & 85.636 & 261 \\ H\_Arch\_C\_8 & **28 K** & **158 KB** & **0.58 G** & 97 & 2 m & 83.93 & 83.158 & **775** \\ \hline MobileNet & 1.66 M & 8.45 MB & 7.6 G & 62 & 2 m 20 s & 72.252 & 79.853 & 234 \\ Fast R-CNN & 130 M & 1224 MB & 195.01 G & **20** & 60 m & 70.2 & 63.800 & 36 \\ YOLOv2 & 63.7 M & 255.81 MB & 128.2 G & 150 & 2 m 40 s & 85.19 & 82.424 & 56 \\ YOLOv2 tiny & 15.04 M & 60.16 MB & 25.3 G & 100 & 2 m 20 s & 78.37 & 75.417 & 137 \\ YOLOv4 & 62.47 M & 244.15 MB & 219.5 G & 36 & 6 m 18 s & 93.47 & 83.036 & 10 \\ YOLOv4 tiny & 6 M & 22.43 MB & 24.9 G & 59 & **57** s & **94.33** & **88.362** & 65 \\ YOLOv51 & 88.3 M & 529.41 MB & 170.46 G & 120 & 2 m 30 s & 92.75 & 87.582 & 62 \\ YOLOv5 s & 7.2 M & 13.91 MB & 25.78 G & 70 & 1 m 48 s & 92.34 & 87.247 & 169 \\ YOLOv5 n & 1.9 M & 3.86 MB & 7.03 G & 60 & 1 m 20 s & 92.12 & 87.582 & 255 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison results on HRSID. Figure 13: F1-confidence. By the above comparisons, some shortages of Darts_tiny receive our attention. First, although the neural networks generated are light and efficient, they do not have the best performance in detection accuracy. Recent YOLO versions win better accuracy, as more techniques and tricks are leveraged, e.g., a \"backbone + Neck + detection head\" structure, an optimized loss function, etc. Integrating successful artificial experiences into the neural architecture search could be meaningful for future accuracy improvement. Second, fewer computations do not always yield a proportionally faster detection speed, as different hardware platforms e.g., GPUs and embedded processing units, have different preferences in the neural architecture and the operations used. It could be the case that a lighter model fails to win an expected faster detection speed when running on a particular hardware. Improving Darts_tiny to be hardware-aware is quite meaningful.