# A Efficient and Accurate UAV Detection Method Based on YOLOv5s
## 2 Related Work
### Lightweight Network  
To enhance the efficiency and capability of portable devices in processing image and video data while adhering to storage space and power consumption constraints, the design of lightweight deep neural network architectures is crucial. In recent years, the design of lightweight neural network architectures has garnered extensive attention from both the academic and industrial communities, resulting in several seminal approaches, categorized into three distinct directions: manual design of lightweight neural network models [15; 32]; automated neural network architecture design via neural architecture search [33]; and neural network model compression [34].  
Significant progress has been made in the design of lightweight neural architectures. For instance, Google introduced the MobileNet architecture, which utilizes depthwise separable convolutions instead of standard convolutions to reduce complexity [35; 36]. Face++ developed ShuffleNet, employing pointwise group convolutions and channel shuffle techniques [37]. MnasNet [38] and NasNet [39] leverage reinforcement learning to automate the construction of lightweight neural networks on portable devices. Notably, NasNet employs a block-based search space that significantly accelerates the search process.  
In [PERSON]'s IGCNets [40], a two-tier grouped convolution approach is utilized to ensure inter-group feature interactions, achieved through varying group sizes and subsequent feature rearrangement. IGCv2 further advances by employing a combination of 3 \(\times\) 3 depthwise convolutions and 1 \(\times\) 1 grouped convolutions for enhanced convolution efficiency, all meeting complement conditions [41]. [PERSON] et al. proposed EspNet [42], which decomposes standard convolutions into 1 \(\times\) 1 pointwise convolutions and spatial pyramid expansion convolutions, reducing parameters and computations while increasing receptive fields. In EspNetV2 [43], dilated convolutions have been replaced with deep convolutions, and 1 \(\times\) 1 pointwise convolutions are substituted with 1 \(\times\) 1 grouped convolutions.  
The widespread use of low, slow, and small unmanned aerial vehicles presents novel challenges to the security of high-value targets. While current deep learning-based detection models have made significant strides in improving recognition accuracy, this often leads to substantial computational resource consumption, resulting in low detection efficiency in practical applications. To address this, we propose the EDU-YOLO (Efficient Detection of UAV-YOLOv5s) algorithm, which is designed to enhance the detection efficiency of small targets through two main modules: lightweight feature extraction and balanced feature fusion.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Algorithms**} & \multicolumn{6}{c}{**Small Object Detection Methods**} & \multicolumn{6}{c}{**Backbone Network**} \\ \cline{2-10}  & \begin{tabular}{c} **Multi-Scale** \\ **Prediction** \\ \end{tabular} & \begin{tabular}{c} **Data Augmentation** \\ **mentation** \\ \end{tabular} & \begin{tabular}{c} **Improving** \\ **Feature** \\ **Resolution** \\ \end{tabular} & \begin{tabular}{c} **Based on** \\ **Contextual** \\ **Information** \\ \end{tabular} & \begin{tabular}{c} **New** \\ **Backbone** \\ **Network** \\ \end{tabular} & \
\begin{tabular}{c} **ResNet–101** \\ **ResNet–101** \\ \end{tabular} &
\begin{tabular}{c} **ResNet–59** \\ **DetNet–59** \\ \end{tabular} \\ \hline SSD513 & \(\surd\) & \(\surd\) & \(\surd\) & & & \(\surd\) & & \\ DSSD513 & \(\surd\) & & \(\surd\) & & & \(\surd\) & & \\ FPN & \(\surd\) & & & & \(\surd\) & & \\ PANet & \(\surd\) & & & & & \(\surd\) & \\ SNIPER & \(\surd\) & & & & \(\surd\) & & \\ CoupleNet & & & & \(\surd\) & & & \\ DetNet & & & & & \(\surd\) & & \(\surd\) \\ DetectoRS & \(\surd\) & & & & & \(\surd\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of different algorithms for small object detection.