# SPL-YOLOv8: A Lightweight Method for Rape Flower Cluster Detection and Counting Based on YOLOv8n
## 4 Results and Analysis
### Comparative Experiments with Different Detection Models  
To compare the detection performance of our model with current mainstream object detection models on the RFRB dataset, we conducted comparative experiments with CenterNet [38], RetinaNet [39], EfficientDet-D0 [31], SSD [40], MobileNet-SSD [41], YOLOv3 [42], YOLOv4 [43], YOLOv5n [44], YOLOv5s, YOLOv6 [45], YOLOv7 [46], YOLOv8n, YOLOv10n [47], YOLOv1n [48], and the RT-DETR [49] series on the same dataset. The results are shown in Table 4. Overall, non-lightweight detection models like CenterNet, RetinaNet, EfficientDet-D0, SSD, MobileNet-SSD, YOLOv3, and YOLOv4 achieve around 80% detection accuracy but have large parameter counts and floating-point operations, with GFLOPs exceeding 100G and much higher model memory usage than lightweight models, making them less suitable for lightweight real-time detection. Lightweight models such as YOLOv5n, YOLOv5s, YOLOv6, YOLOv7, YOLOv8n, YOLOv10n, and YOLOv11n significantly reduce computational complexity and memory usage while maintaining high detection accuracy. Although the RT-DETR series has a slight edge in detection accuracy, with AP\({}_{50}\) values of 92.7% and 92.9% for RT-DETR-r18 and RT-DETR-r34, respectively, their GFLOPs and parameters are relatively high at 56.9G/18.95 and 88.8 G/29.67, with model sizes of 40.5 MB and 63.0 MB, making them less effective for model lightweighting compared to YOLO series and SPL-YOLOv8n series models. The pruned SPL-YOLOv8-prune, with its lightweight StarNet backbone and C2f-Star module, has a parameter count that is only 7.32%, 1.97%, 3.23%, 2.22%, 4.54%, 6.11%, 5.37%,  
Figure 10: Comparison of channels before and after pruning.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Pruning** & \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Pruning Rate**} & \multirow{2}{*}{**AP\({}_{50}\)/\%**} & \multirow{2}{*}{**Parameters/M**} & \multirow{2}{*}{**GFLOPs/G**} & \multirow{2}{*}{**Model Size/MB**} & \multirow{2}{*}{**fps**} \\ \cline{1-1}
**Method** & & & & & & & \\ \hline SPL-YOLOv8 & / & / & 92.4 & 1.24 & 3.4 & 2.8 MB & 166.2 \\ \hline \multirow{4}{*}{lamp} & EXP1 & 1.5 & 92.3 (\(-\)0.1\%) & 0.49 (39.5\%) & 2.2 (64.7\%) & 1.3 MB & 141.3 (\(-\)14.9\%) \\  & EXP2 & 2.0 & 92.2 (\(-\)0.2\%) & 0.27 (21.7\%) & 1.7 (50\%) & 0.8 MB & 167.7 (\(+\)0.9\%) \\  & EXP3 & 2.5 & 91.4 (\(-\)1.0\%) & 0.18 (14.5\%) & 1.3 (38.2\%) & 0.6 MB & 169.6 (2.0\%) \\  & EXP4 & 3.0 & 92.2 (\(-\)0.2\%) & 0.13 (10.4\%) & 1.1 (32.3\%) & 0.5 MB & 171.1 (\(+\)2.9\%) \\ \hline \multirow{4}{*}{11} & EXP1 & 1.5 & 85.7 (\(-\)7.2\%) & 0.98 (79.3\%) & 2.9 (85.3\%) & 2.6 MB & 187.5 (\(+\)12.8\%) \\  & EXP2 & 2.0 & 88.6 (\(-\)4.1\%) & 0.92 (74.2\%) & 1.7 (50.0\%) & 2.3 MB & 176.5 (\(+\)6.2\%) \\  & EXP3 & 2.5 & 88.7 (\(-\)4.0\%) & 0.88 (71.0\%) & 0.88 (25.8\%) & 2.1 MB & 180.3 (\(+\)8.5\%) \\  & EXP4 & 3.0 & 87.3 (\(-\)5.5\%) & 0.72 (58.1\%) & 0.72 (21.2\%) & 1.8 MB & 182.8 (\(+\)10.0\%) \\ \hline \multirow{4}{*}{Group-Sl} & EXP1 & 1.5 & 90.6 (\(-\)1.9\%) & 0.79 (63.7\%) & 2.3 (67.7\%) & 2.2 MB & 167.2 (\(+\)0.6\%) \\  & EXP2 & 2.0 & 91.3 (\(-\)1.2\%) & 0.59 (47.6\%) & 1.7 (50.0\%) & 1.5 MB & 165.9 (\(-\)0.2\%) \\ \cline{1-1}  & EXP3 & 2.5 & 89.8 (\(-\)2.8\%) & 0.45 (36.3\%) & 1.3 (38.2\%) & 1.3 MB & 160.2 (\(-\)3.7\%) \\ \cline{1-1}  & EXP4 & 3.0 & 91.2 (\(-\)1.3\%) & 0.34 (27.4\%) & 1.1 (32.3\%) & 1.0 MB & 174.7 (\(+\)5.1\%) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of results under different pruning methods.  
and 10.65% of that of YOLOv5n, YOLOv5s, YOLOv6, YOLOv7, YOLOv8n, YOLOv10n, YOLOv11n, and other models, respectively. It also significantly reduces the number of parameters and floating-point operations, making the model more lightweight and better suited for real-time detection.