## 3 Experiments and Analyses
### Performance Comparison of Different Object Detection Algorithms  
In order to verify the superiority of the improved network relative to other object detection algorithms, this paper analyzed several current mainstream algorithms, including the two-stage object detection algorithm Faster RCNN, YOLOv5s, YOLOv4-tiny, and the YOLOv5s-EfficientNet network, run under the experimental environment configuration in Table 1, and the results are shown in Table 4.  
As can be seen from Table 4, the two-stage object detection algorithm Faster RCNN is slow in inference because it has two stages of candidate region generation and feature extraction. In addition, the fully connected network used by this model occupies a large number of parameters and does not meet the real-time detection requirements. Compared with the other YOLO series algorithms in Table 4, YOLO v4-tiny has the largest model size, which seriously affects the computation, the YOLOv5s-EfficientNet algorithm, which is based on the lightweight improvement of YOLOv5s, does not differ much in the model  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Improvement Factors**} & \multicolumn{3}{c}{**Evaluation Indicators**} \\ \cline{2-9}  & \begin{tabular}{c} **Backbone** \\ **Improvement** \\ \end{tabular} & \
\begin{tabular}{c} **SIoU** \\ **SloU** \\ \end{tabular} &
\begin{tabular}{c} **PAN** \\ **Improvement** \\ \end{tabular} & **P/\%** & **R/\%** & **[EMAIL_ADDRESS]/\%** & **F\({}_{1}\) Score** \\ \hline YOLOv5s & \(\times\) & \(\times\) & \(\times\) & 98.2 & 97.7 & 98.4 & 97.94 \\ YOLOv5s-B & \(\surd\) & \(\times\) & \(\times\) & 96.3 & 95.4 & 98.1 & 95.84 \\ YOLOv5s-B-P & \(\surd\) & \(\times\) & \(\surd\) & 97.2 & 95.1 & 98.5 & 96.14 \\ YOLOv5s- & \(\surd\) & \(\surd\) & \(\surd\) & 98.6 & 97.4 & 98.8 & 97.99 \\ \hline \hline \end{tabular} Backbone improvement includes the introduction of the unit of ShuffleNetV2, ECA and GELU. “\(\times\)” means not to use the improvement factor, “\(\surd\)” means to use the improvement factor.  
\end{table}
Table 3: Analysis of model effectiveness.  
Figure 9: [EMAIL_ADDRESS] curve.  
volume and detection accuracy performance indexes, but its overall performance is still inferior to the final improved algorithm YOLOv5s-Camellia in this paper. The final improved model in this paper improves the [EMAIL_ADDRESS] value by 0.4 percentage points compared with the original YOLOv5s network, increases the detection speed by 57.74%, reduces the average single image detection time and model volume by more than 50%, meets the real-time detection requirements, and saves the computational cost.  
Figure 10 compares the actual detection performance of different object detection algorithms in Table 4. From the detection effect graph, it can be seen that the YOLOv5s-Camellia model proposed in this paper has high regression accuracy, the best detection effect for fruit overlap and small targets in the distant view, and the edge detection ability for _Camellia oleifera_ fruit is stronger than other target detection algorithms. In summary, the detection algorithm proposed in this paper has stronger robustness in the natural environment.  
Figure 10: Comparison of recognition results of four target detection algorithms: (**a**) YOLOv5s, (**b**) YOLO v4-tiny, (**c**) YOLOv5s-EfficientNet, (**d**) Faster R CNN, and (**e**) YOLOv5s-Camellia.