# SMYOLO: Lightweight Pedestrian Target Detection Algorithm in Low-Altitude Scenarios
## 5 Conclusions  
This paper proposes a pedestrian detection algorithm based on deep separable convolution in the UAV scene, which mainly solves the problem of the limited computing power of embedded devices. Secondly, it solves the problem of missed detection of lightweight networks. This algorithm replaces the YOLOv4 conventional convolution with a depth separable convolution, increases the network depth, reduces the network width, and makes the network architecture \"narrow and deep\", which greatly reduces the number of model parameters and improves the detection efficiency. At the same time, the BN layer and multi-scale training methods are used to improve the model's accuracy. In addition, we use the scale matching method to solve the problem of feature representation loss caused by scale mismatch, which increases the detection accuracy of SMYOLO to a certain extent. We tested SMYOLO in the same environment. Compared with other detection algorithms, SMYOLO performed more prominently. To ensure the detection accuracy of the model, the detection model has a good detection effect on the target detection on the mobile end of the UAV. We expect to get a faster model while ensuring the same accuracy in the future.  
**Author Contributions:** Conceptualization, W.Z., X.M. and Y.Z.; funding acquisition, W.Z.; investigation, X.M., M.J. and C.Z.; methodology, W.Z. and X.M.; project administration, W.Z.; software, X.M.; supervision, Y.Z., M.J. and C.Z.; Writing--original draft, X.M.; Writing--review and editing, X.M. All authors have read and agreed to the published version of the manuscript.  
Figure 9: FPS and mAP comparison with mainstream target detection algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Input** & **Model Volume(M)** & **Precision/\%** & **Recall/\%** & **mAP/\%** & **FPS** \\ \hline SSD300 & \(300\times 300\) & 100 & 73 & 57 & 64.53 & 28.56 \\ SSD512 & \(512\times 512\) & 104 & 69 & 63 & 68.09 & 16.44 \\ YOLOv3 & \(416\times 416\) & 237 & 72.46 & 49.82 & 56.83 & 54.7 \\ YOLOv4 & \(416\times 416\) & 245 & 76 & 67 & 72.45 & 40.8 \\ YOLOv4 Tiny & \(416\times 416\) & 23.1 & 68.37 & 38.97 & 43.82 & 65.6 \\ SlimYOLOv3 & \(416\times 416\) & 79.6 & 63.4 & 35.3 & 40.5 & 43.1 \\ SMYOLO:Before SM & \(416\times 416\) & 56.6 & 72.4 & 51.68 & 58.6 & 57.24 \\ SMYOLO:After SM & \(416\times 416\) & 56.6 & 69.4 & 63.21 & 68.09 & 54.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between the proposed method and advanced detection algorithms.  
**Funding:** This research was funded by the Natural Science Foundation of China (Grant No. 61976098), the Technology Development Foundation of Quanzhou City (Grant No. 2020C067), Key Science and Technology Project of Xiamen City (Grant No. 3502 ZCQ20201008).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** This study analyzes publicly available datasets. This datasets can be found here: [[http://aiskyeye.com/download/](http://aiskyeye.com/download/)]([http://aiskyeye.com/download/](http://aiskyeye.com/download/)) (accessed on 1 December 2021).  
**Conflicts of Interest:** The authors declare no conflict of interest.