# Edge-YOLO: Lightweight Infrared Object Detection Method Deployed on Edge Devices
## 4 Experiments
### Ablation Experiments  
In this part, the original ShuffleNetv2 is firstly used as the backbone network of YOLOv5m, based on which the ablation experiments of several improvement strategies proposed in this paper are conducted to better understand the effects of different improvement strategies on the detection effect in Edge-YOLO, and the results are shown in Table 2 below.  
As can be seen from Table 2, compared with the first group of experiments using only the basic model, the second group of experiments with the addition of EX-IoU solves the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Params/M** & **Flops/G** & **Size/MB** \\ \hline YOLOv5m & 20.9 & 47.9 & 42.2 \\ Edge-YOLO & 5.8 & 14.2 & 12.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of lightweight improvement effects.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**+EX-IoU**} & \multicolumn{1}{c}{**+CAU-Lite**} & \multicolumn{1}{c}{**+SDCA**} & \multicolumn{1}{c}{**[EMAIL_ADDRESS]/\%**} & \multicolumn{1}{c}{**FPS**} & \multicolumn{1}{c}{**Params/M**} & \multicolumn{1}{c}{**Flops/G**} & \multicolumn{1}{c}{**Size/MB**} \\ \hline \multirow{3}{*}{\(\surd\)} & & & 73.1 & 85.3 & 5.4 & 12.9 & 11.3 \\  & \(\surd\) & & 74.3 & 85.1 & 5.4 & 12.9 & 11.3 \\  & \(\surd\) & & 74.7 & 84.0 & 5.4 & 13.1 & 11.4 \\  & \(\surd\) & \(\surd\) & 76.2 & 81.5 & 5.8 & 14.1 & 12.0 \\ \(\surd\) & \(\surd\) & \(\surd\) & 78.8 & 80.7 & 5.8 & 14.2 & 12.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation experiments.  
Figure 6: Variation in accuracy for different values of \(\alpha\) on the algorithm.  
problem of uncertainty in the aspect ratio of CIoU by improving the loss function of the bounding box and accelerating the convergence of the loss function, and the detection accuracy is improved by 1.2% from the results, while the remaining parameters remain unchanged. The third group of experiments replaces the original nearest neighbor interpolation up-sampling with the CAU-Lite up-sampling operator proposed in this paper, which senses and aggregates contextual information within a larger reception field, dynamically generates adaptive up-sampling kernels, and performs feature reorganization based on the generated up-sampling kernels. It can be seen that with CAU-Lite, the detection accuracy of the model is improved by 1.6%, but the FPS is also slightly reduced. The fourth group of experiments applies the strip depthwise convolutional attention module proposed in this paper, which replaces the original ShuffleNetv2 network structure with an improved ShuffleBlock, enhancing the feature extraction capability for strip-shaped targets and the perception of the saliency of infrared targets. As seen in the table, the detection performance of the model is significantly improved by 3.1% compared with the original model, but the number of parameters, computation, and size of the model increased due to the addition of the new module. The final fifth set of experiments uses a combination of the three improvement points proposed in this paper, and from the results, a larger performance improvement is obtained at the cost of fewer computational and storage resources compared with the original model.  
Figure 7 below shows the P-R curves of each class of different improvement strategies applied to the base model and the complete Edge-YOLO. The figure shows that compared with the base model, the APs of all three target categories are improved with different improvement strategies, and the AP of the bicycle category is improved most significantly.