## 3 Experimental Results and Discussion
### Experimental Results
#### 3.3.4. Optimization of Module Ablation Experiment  
Ablation experiments were performed to evaluate the contribution of the four modules to improved model performance. SCSA, Uni-IOU, MobileNetV4, and ASF-SDI are added to the model in this article in turn. The experimental design is shown in Table 5, while the 3D scatter plot of the experimental data is shown in Figure 12, where G1 to G9 denote Experiment 1 to Experiment 7, respectively:  
\begin{table}
\begin{tabular}{c c c c} \hline
**YOLOvs** & **YOLO-SUMAS (Ours)** & **YOLOvs** & **YOLO-SUMAS (Ours)** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Prediction contrast chart.  
SCSA enhances the model's ability to focus on key semantic features, dynamically adjusts feature weights through the channel and spatial attention, and suppresses irrelevant noise; further, its mAP@50 value verifies its advantage in target localization accuracy. Unified-IoU is less accurate than SCSA when used alone and has limited improvement in boundary regression when applied independently as a loss-function optimization module, but its accuracy is close to SCSA's. In terms of reducing false detections, it relies on other modules to provide high-quality feature inputs. MobileNetV4 has the highest recall rate among all modules, and its Universal Inverted Bottleneck structure efficiently extracts multi-scale features to improve small target detection. ASF-SDI cross-scale feature fusion strategy introduces redundancy information or gradient information when there is a lack of attention or efficient backbone support. It introduces redundant information or gradient conflicts, and the accuracy is relatively low.  
Figure 12: 3D scatter plot.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **SCSA** & **Unified-IoU** & **MobileNetV4** & **ASF-SDI** & **P (\%)** & **Recall (\%)** & **mAP50 (\%)** & **F1** \\ \hline Exp1 & ✓ & \(\times\) & \(\times\) & \(\times\) & 97.2 & 94.7 & 97.5 & 0.96 \\ Exp2 & ✗ & ✓ & \(\times\) & \(\times\) & 97 & 92.5 & 94.5 & 0.95 \\ Exp3 & ✗ & \(\times\) & ✓ & \(\times\) & 96.6 & 95.5 & 97.7 & 0.96 \\ Exp4 & ✗ & \(\times\) & \(\times\) & ✓ & 96.7 & 88.8 & 94.9 & 0.92 \\ Exp5 & ✓ & ✓ & \(\times\) & \(\times\) & 98.2 & 94.4 & 97.6 & 0.96 \\ Exp6 & ✓ & ✓ & ✓ & \(\times\) & 97.9 & 98.1 & 98.7 & 0.98 \\ Exp7 & ✓ & ✓ & ✓ & ✓ & 98.8 & 99.2 & 99.1 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation data.  
2. The analysis of the module synergistic effect The accuracy rate is improved to 98.2% in Experiment V. The attention mechanism of SCSA complements the boundary optimization of Unified-IoU; the former enhances the feature discriminative properties, and the latter refines the localization accuracy. After MobileNetV4 is added based on Experiment V, both recall and map@50 are greatly improved. The Universal Inverted Bottomed V4 structure of MobileNetV4 and the MobileMQA module efficiently deliver multi-level features to make up for SCSA's shortcomings in high-level semantic fusion and form a strong synergy with it, while Unified-IoU optimization lightsens the localization deviation of the backbone. Precision, recall, and mAP@50 peak in Experiment 7. SCSA enhances local feature discrimination and provides a high-quality feature base for other modules; MobileNetV4 provides efficient multi-scale characterization; ASF-SDI fuses details and semantics through cross-layer feature products in the complete framework; and Unified-IoU refines the loss computation. The four form a closed loop of \"feature extraction-attention focusing-multi-scale fusion-loss optimization\" to achieve a comprehensive performance breakthrough. Its precision-confidence curve (Figure 13), recall rate-confidence curve (Figure 14), and mAP@50 curve (Figure 15) are as follows, where a-g in Figure 13 represents experiments I to VII; a-g in Figure 14 represents experiments I to VII; and exp1 to exp7 in Figure 15 represents experiments I to VII in tables.