## 3 Experiments and Analyses
### Ablation Experiments  
In response to the existing problems of the complex network structure of the _Camellia oleifera_ fruit object detection algorithm, slow inference speed of mobile terminal, and high cost of memory usage, we conducted experiments and analysis on the improved method based on YOLOv5s. The experimental results are shown in Table 2.  
The YOLOv5s network model has been lightened and improved, and the FLOPs, number of parameters, size, and number of network layers have been significantly reduced, according to the ablation comparison test in Table 2. It can be seen that after the backbone network was reconstructed by the unit of ShuffleNetv2, the number of convolutional kernels was reduced due to the lightweight improvement of the network, while the network's ability to extract the detailed features of _Camellia oleifera_ fruit was weakened, and the [EMAIL_ADDRESS] was reduced by 1.5%. In order to improve the model accuracy, three ECA channel attention modules were introduced in the backbone network to enhance the channel features of \begin{table}
\begin{tabular}{c c} \hline \hline
**Designation** & **Environment Configuration** \\ \hline Operating System & Windows11 \\ CPU & Intel(R) Core(TM) i5-9400F \\ GPU & GeForceRTX3070 Ti(8 GB) \\ Development Framework & Pytorch1.7.1 \\ Development Environment & Anaconda, Python3.9, CUDA11.3, OpenCV4.5.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental platform configuration. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **FLOPs/(G)** & **Parameters** & **Size/MB** & **Layers** & **[EMAIL_ADDRESS]/\%** \\ \hline YOLOv5s & 15.8 & 7,012,822 & 14.5 & 213 & 98.4 \\ YOLOv5s + ShuffleNet & 5.3 & 2,898,610 & 6.2 & 184 & 96.9 \\ YOLOv5s + ShuffleNet + ECA & 5.7 & 3,137,679 & 6.7 & 187 & 98.1 \\ YOLOv5s + ShuffleNet + ECA + Add & 5.5 & 2,973,839 & 6.3 & 187 & 98.2 \\ YOLOv5s + ShuffleNet+ECA + Add + GELU & 5.5 & 2,973,839 & 6.3 & 187 & 98.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Lightweight ablation comparison experiment. the input feature map, which increased the [EMAIL_ADDRESS] by 1.2% with an 8.25% increase in computation. The Concat splicing operation in the PAN was changed to the Add operation. Add dimensional fusion increased the amount of information in each dimension of a feature map without expanding the tensor dimension and achieved a 0.1% improvement in [EMAIL_ADDRESS] while reducing the number of model parameters and computational effort. In addition, the GELU activation function was introduced into the Shuffle Block in Figure 3 to optimize the network, which enhanced the nonlinear expressivity of the network. The final detection [EMAIL_ADDRESS] reaches 98.5%, compared with the YOLOv5s model before the improvement. In terms of FLOPs, the number of parameters and model size were reduced by 65.19%, 57.59%, and 56.55%, respectively, and the number of network layers was reduced by 26 layers, and the [EMAIL_ADDRESS] was increased by 0.1 percentage point, which improved the performance of _Camellia oleifera_ fruit real-time detection. In order to verify that the SIoU loss function outperforms the CIoU in the _Camellia oleifera_ fruit dataset and can perform the task of real-time detection in the complex natural environment after the improvement of the backbone network, the PAN and the optimization of the activation function, the loss values between the ground truth box and the predicted box of the model training phase and the validation phase were selected as the evaluation index of the convergence performance of the improved model, respectively, and the loss curves of the final version of the improved model after 100 iterations are shown in Figure 8.  
As shown in Figure 8, the SIoU loss function has better performance in terms of convergence speed and optimized loss value, both in the training and validation phases.