# Improving Moving Insect Detection with Difference of Features Maps in YOLO Architecture
## 3 Experimental Results and Discussion

Table 4: Quantitative results of the YOLOv8s model with the motion module inserted at different backbone positions (\(B_{1}\) to \(B_{9}\)). The table reports Precision, Recall, F1 Score, and mAP50 on the validation set for each configuration. The baseline row corresponds to the original YOLOv8s model without the motion module. 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 YOLOv8n Baseline & 85.92\% & 83.09\% & 84.48\% & 87.44\% & 8.08 \\ YOLOv8n [29] & 85.14\% & 88.67\% & 86.87\% & 93.93\% & 8.08 \\ YOLOv8n + Motion module & 84.79\% & 89.31\% & 87.00\% & 91.20\% & 8.89 \\ YOLOv8s Baseline & 86.64\% & 85.76\% & 86.20\% & 90.56\% & 28.44 \\ YOLOv8s [29] & **88.20\%** & 87.81\% & 88.00\% & **94.59\%** & 28.44 \\ YOLOv8s + Motion module & 87.30\% & **89.50\%** & **88.39\%** & 93.19\% & 31.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between the proposed motion module and the method from [29] across YOLOv8n and YOLOv8s, we report the Precision, Recall, F1 Score, mAP50 and the number of GFLOPs. Bold font indicates best values. lighting variations. In addition, our approach operates with only two frames, in contrast to [29], which relies on three. As mentioned earlier, we conducted a study to evaluate the impact of using the absolute value of feature map difference versus raw residuals. The results show that using the absolute value leads to a 0.24% increase in F1 Score and a 0.17% improvement in mAP50 for YOLOv8n, and a 0.12% increase in F1 Score and a 0.64% improvement for mAP50 for YOLOv8s, compared to using raw residuals. These findings suggest that emphasizing the magnitude of motion changes, regardless of direction, helps the network extract more discriminative features. Table 6 shows that although using raw residuals results in slightly higher precision, applying the absolute value leads to improvements in recall, F1 Score and mAP50. In our approach, we prioritize higher recall to reduce false negatives, as failing to detect a relevant insect can be critical in agricultural monitoring. To evaluate the robustness of our approach to lighting variations, we tested both our method and the approach from [29] on the original test set after applying modifications to each input frame. Specifically, we randomly adjusted the HSV (Hue, Saturation, and Value) components to simulate different illumination conditions, such as changes in brightness and color tone. The results of this evaluation are presented in Table 7. This allows us to assess the robustness of each approach in more challenging, real-world-like conditions. Table 7 shows that both YOLOv8n and YOLOv8s models enhanced with our motion module outperform their counterparts using the method proposed in [29]. These results demonstrate that our method is more robust to lighting variations, as motion integration is performed at the feature map level. In contrast, ref. [29] relies on frame differencing, a technique that is highly sensitive to changes in illumination. This sensitivity is reflected in the significant performance drop observed for models using [29] under varying lighting conditions. Finally, we present qualitative results for both YOLOv8n and YOLOv8s models enhanced with the motion module. Figure 12 shows predictions from the YOLOv8n variant, while Figure 13 shows results from YOLOv8s. These examples illustrate how the integration of the motion module enhances the ability of the model to localize and detect objects