## 4 Experimental Design and Results Analysis
### Experimental Results and Analysis  
#### 4.4.1 Improved YOLOv8s Results and Analysis  
To verify the effectiveness of a network that is lightweight after C2f is replaced by CGC2f, various mainstream backbone networks are introduced on the basis of YOLOv8s. GhostNetV2 [26], MobileNetV3 [27], and FasterNet [28] are used to replace the backbone network of YOLOv8s as the lightweight YOLOv8s model and compared with the improved CGC2 fNet model in this paper. The CSPDarkNet model is the initial backbone network model of YOLOv8s. In the table, GFLOPs mean floating point operations per second measured in giga. Model complexity increases with GFLOPs. Table 2 shows that with the addition of the GhostNetV2 network, although the network's GFLOPs are reduced by 10.4 and the precision is improved, the quantity of pictures processed in a second is reduced by 29 FPS, mAP50 is reduced by 0.1%, and the recall rate is reduced by 1.3%. This is because it increases memory access and adds additional data operations, resulting in a reduction in the speed of image processing per second. After adding the FasterNet network, FPS was significantly improved, but mAP dropped by 0.2%, and recall dropped by 1.4%. Although it introduced Partial Convolution (PConv), it reduced the amount of calculation by processing only part of the input channel. Memory access may result in the loss of some spatial features. After adding the MobileNetV3 network, although the calculation amount is reduced, the FPS decreases, and the memory access increases like the GhostNetV2 network. For the improved CGC2f module, GFLOPs are reduced by 5.3, mAP is increased by 0.4%, the quantity of images handled per second is increased by 30.5 FPS, and detection speed and accuracy are improved. Through analysis, the improved CGC2f module improves throughput while reducing computational complexity, memory access and computational latency, balancing lightness and model performance. In order to improve the accuracy of the network model further, the ESE attention mechanism module has been added to the improved model. Adding the module will boost the amount of network layers and parameters. For the purpose of removing the effects of adding the ESE module on the training accuracy, the ESE module is replaced by the CA, CBAM modules and SE modules, respectively, while keeping other conditions unchanged, and ablation experiments are performed. Table 3 presents the experimental findings, where Layers are the number of network layers, and Parameters are the number of network parameters. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Backbone** & \multirow{2}{*}{**GFLOPs**} & \multirow{2}{*}{**P (\%)**} & \multirow{2}{*}{**R (\%)**} & \multirow{2}{*}{**mAP50 (\%)**} & \multirow{2}{*}{**FPS**} \\
**Network** & & & & & \\ \hline CSPDarkNet & 28.5 & 94.3 & 89.7 & 94.2 & 177.8 \\ GhostNetV2 & 18.1 & 96.6 & 88.4 & 94.1 & 148.8 \\ FasterNet & 21.7 & 94.8 & 88.3 & 94.0 & 208.8 \\ MobileNetV3 & 20.4 & 94.8 & 89.4 & 94.2 & 162.3 \\ CGC2 fNet & 23.2 & 94.8 & 89.0 & 94.6 & 207.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of lightweight backbone networks. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & \multirow{2}{*}{**Layers**} & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**GFLOPs**} & \multirow{2}{*}{**P (\%)**} & \multirow{2}{*}{**R (\%)**} & \multirow{2}{*}{**mAP50 (\%)**} \\
**(M)** & & & & & \\ \hline CGC2 fNet & 222 & 9.3 & 23.2 & 94.8 & 89.0 & 94.6 \\ +CA & 262 & 9.3 & 23.2 & 94.6 & 90.8 & 95.1 \\ +CBAM & 254 & 9.7 & 23.2 & 95.3 & 90.5 & 94.9 \\ +SE & 250 & 9.3 & 23.2 & 95.9 & 88.7 & 94.6 \\ +ESE & 234 & 9.7 & 23.2 & 96.1 & 90.1 & 95.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ESE attention module ablation experiment. According to Table 3, compared with other attention mechanism modules, the ESE module only introduces a small number of layers, parameters and calculations.