# Improving Moving Insect Detection with Difference of Features Maps in YOLO Architecture
## 3 Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1 Score**} & \multicolumn{2}{c}{**mAP50**} \\ \cline{2-9}
**Model** & **Raw** & **Abs.** & **Raw** & **Abs.** & **Raw** & **Abs.** & **Raw** & **Abs.** \\  & **Residuals** & **Value** & **Residuals** & **Value** & **Residuals** & **Value** & **Residuals** & **Value** \\ \hline YOLOv8n & **86.28**\% & 84.79\% & 87.25\% & **89.31**\% & 86.76\% & **87.00**\% & 91.03\% & **91.20**\% \\ YOLOv8s & **87.58**\% & 87.30\% & 88.97\% & **89.50**\% & 88.27\% & **88.39**\% & 92.55\% & **93.19**\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of detection performance using raw residuals versus using their absolute value for both YOLOv8n and YOLOv8s. Bold font indicates best values. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1 Score** & **mAP50** \\ \hline YOLOv8n [29] & 78.39\% & 61.39\% & 68.85\% & 69.03\% \\ YOLOv8n + Motion module & **88.60\%** & **83.09\%** & **85.76\%** & **90.29\%** \\ YOLOv8s [29] & 81.59\% & 62.45\% & 70.75\% & 71.36\% \\ YOLOv8s + Motion module & 85.66\% & 70.46\% & 77.32\% & 79.50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of detection performance under simulated lighting variations using HSV transformations applied on the test set. The table reports Precision, Recall, F1 Score and mAP50 for both our method and the approach from [29]. Bold font indicates best values. in challenging scenarios, such as cases involving small objects by leveraging motion-based feature information. As shown in Figure 8, some image pairs contain visible motion in the background, particularly in plant parts such as leaves or flowers, likely caused by wind. Despite this, the model achieves accurate detections, this can be attributed to the fact that motion is computed at the feature map level rather than from raw pixel intensities. By performing temporal differencing on feature maps, the network can focus on meaningful patterns while suppressing irrelevant changes. Subsequent layers in the network further refine these feature maps, retaining only representations that are useful for insect detection. After demonstrating that the motion module improves honeybee detection performance, and exhibits greater robustness to lighting variations than the method proposed in [29], which relies on frame differencing, we now evaluate the proposed method on the synthetic pest dataset to assess its performance on a broader range of insect types. This evaluation complements the previous analysis on the honeybee dataset and provides  
Figure 12: Detection results using YOLOv8n enhanced with the proposed motion module. Figure 13: Detection results using YOLOv8n enhanced with the proposed motion module. insights into the ability of the model to generalize across different insect classes. Below, we present the quantitative results and comparisons with the baseline YOLOv8 models, highlighting the effectiveness of the motion module in this new setting. Based on the findings from the honeybee dataset experiments, where the insertion of the motion module after block \(B_{3}\) in the network backbone yielded a favorable trade-off between accuracy and computational cost, we place the motion module in this position for all experiments conducted on the synthetic pest dataset. This placement aims to preserve a consistent configuration while allowing the network to integrate motion features at a sufficiently abstract level. The results in Table 8 demonstrate the effectiveness of integrating the motion module into the YOLOv8n backbone for pest detection. While the baseline YOLOv8n achieves a slightly higher precision, the model with the motion module outperforms the baseline across all other key metrics: recall increases by 4.66%, F1 Score improves by 2.66% and mAP50 rises by 3.49%.