# Improving Moving Insect Detection with Difference of Features Maps in YOLO Architecture
## 3 Experimental Results and Discussion
The higher recall and F1 Score indicate that the motion-enhanced model is more sensitive and effective at detecting pests, including those that may be partially occluded or visually similar to background elements. These improvements are particularly meaningful for pest detection, since failing to detect an insect (i.e., false negatives) can be more critical than occasional false positives. The increased performance is achieved with only a modes increase in computational cost, an additional 0.81 GFLOPs, making the motion module a practical addition even for resource-constrained environments. Now we extend the analysis to YOLOv8s, these results are shown in Table 9. When integrating the motion module into the YOLOv8s backbone for pest detection, we observe improvements across all metrics. Compared to the baseline, the model with the motion module increases precision by 1.16%, recall by 1.98%, F1 Score by 1.72 percentage points, and mAP50 by 3.28 points. These results demonstrate that even for a larger model, the incorporation of motion information remains beneficial. Despite moderate increase in computational cost, an increase of 3.03 GFLOPs, the performance improvements justify the inclusion of the module. Table 10 compares the F1 Score and mAP50 between Baseline YOLOv8 and YOLOv8 with motion module integrated after block \(B_{3}\), for both YOLOv8n and YOLOv8s variants. Results are shown for each pest class individually, showing that the inclusion of the motion module yields improvements in nearly all pest categories, particularly for smaller insects, such as broad mite, suggesting that the motion cues are especially beneficial when visual \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1 Score** & **mAP50** & **GFLOPs** \\ \hline YOLOv8n Baseline & **97.65**\% & 73.92\% & 83.82\% & 82.03\% & 8.09 \\ YOLOv8n + Motion module & 96.65\% & **78.58\%** & **86.48\%** & **85.52\%** & 8.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Detection performance comparison on the synthetic pest dataset, averaged across all classes, between baseline YOLOv8n and YOLOv8n with motion module integrated after block \(B_{3}\). Bold font indicates best values. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1 Score** & **mAP50** & **GFLOPs** \\ \hline YOLOv8s Baseline & 96.66\% & 77.39\% & 85.74\% & 84.52\% & 28.45 \\ YOLOv8s + Motion module & **97.82\%** & **79.37\%** & **87.46\%** & **87.80\%** & 31.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Detection performance comparison on the synthetic pest dataset, averaged across all classes, between baseline YOLOv8 and YOLOv8s with motion module integrated after block \(B_{3}\). Bold font indicates best values. features alone are insufficient. In summary, the per-class analysis confirms the general effectiveness of the motion module across a variety of pest types. Finally, we include qualitative results on the synthetic pest dataset. Figures 14 and 15 show sample predictions from the YOLOv8n and YOLOv8s variants, respectively. The visualization shows that motion integration enhances detection accuracy in scenarios where appearance alone may be insufficient, such as small or camouflaged insects.