# AIR-YOLOv3: Aerial Infrared Pedestrian Detection via an Improved YOLOv3 with Network Pruning
## 5 Conclusions  
Large models, limited memory, and computing power of embedded devices restrict the deployment of aerial pedestrian detection algorithms on the UAV platform. Therefore, an improved infrared aerial YOLOv3 (AIR-YOLOv3) is proposed. Firstly, given the aerial pedestrian dataset, the more suitable prior boxes are reclustered. Secondly, we impose the channel-wise sparsity on trained YOLOv3 model by introducing Smooth-L1 regularization on scale factors. Third, channel pruning was performed on the sparse model. Fourth, based on the channel pruned model, pruning layers were implemented to further compress the model. Finally, the compressed model was deployed on the airborne platform with NVIDIA Jetson TX2. Experiments have proved that the AIR-YOLOv3 achieves a great  
Figure 9: AIR-YOLOv3 test on TX2.The class of the target is person.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**AP (\%)**} & \multicolumn{2}{c}{**Inference Time (FPS)**} & \multirow{2}{*}{
\begin{tabular}{c} **Model Size** \\ **(MB)** \\ \end{tabular} } \\  & & **PC(GTX1660 Ti)** & & \\ \hline Mask R-CNN [16] & 95.5 & 7.9 & 0.7 & 351.0 \\ YOLOv3 [9] & 93.2 & 35.6 & 3.7 & 238.9 \\ Tiny-YOLOv3 [9] & 73.4 & 136.9 & 28.8 & 28.1 \\ AIR-YOLOv3 & 91.5 & 51.5 & 8 & 10.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the AIR-YOLOv3 model on PC and TX2.  
balance of system detection accuracy and speed, and the model is conducive to deployment on airborne mobile computing platforms.  
Network pruning is an effective technique to deploy the large model on mobile devices. It is of great significance to the actual deployment of artificial intelligence algorithms. Choosing a better basic algorithm or a better pruning technique is an important research direction in the future. In addition, future work may be dedicated to pruning the network in a simpler and more flexible way and further exploring the CNN model acceleration technology on specific processor platforms.  
Conceptualization, Y.S. and X.Z. ([PERSON]); methodology, Y.S.; software, X.Z. ([PERSON]) and D.Z.; validation, Y.S. and X.Z. ([PERSON]); formal analysis, Y.S. and X.Z. ([PERSON]); investigation, Y.S. and X.Z. ([PERSON]); resources, Y.S. and H.C.; data curation, X.Z. ([PERSON]); writing--original draft preparation, X.Z. ([PERSON]); writing--review and editing, H.C. and X.Z. ([PERSON]); visualization, X.Z. ([PERSON]); supervision, H.C. and Y.R.; project administration, Y.S.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation (NSFC) of China under Grant No. 61601382, Sichuan Provincial Science and Technology Support Project No. 2019 YJ0325 and No. 2021 YFG0314.  
More data and results are openly available in github at [[https://github.com/sunriselqq/IRA-YOLOv3](https://github.com/sunriselqq/IRA-YOLOv3)]([https://github.com/sunriselqq/IRA-YOLOv3](https://github.com/sunriselqq/IRA-YOLOv3)) (Accessed: 27 March 2022).  
The authors would like to thank the Senior AI Researcher [PERSON] in Sichuan Changhong Electric Co., Ltd., and all reviewers for very helpful comments and suggestions.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} IR & Infrared \\ AIR & Aerial infrared \\ UAV & Unmanned aerial vehicle \\ YOLO & You Look Only Once \\ FPS & Frames per second \\ \end{tabular}