# 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} \hline \hline
**Motion** & & & & & & \\
**Module** & **Precision** & **Recall** & **F1 Score** & **mAP50** & **Params** & **GFLOPs** \\
**Position** & & & & & & \\ \hline Baseline & 87.67\% & 81.54\% & 84.49\% & 86.95\% & 3.00 & 8.08 \\ \(B_{1}\) & 86.79\% & 89.07\% & 87.91\% & 91.69\% & 3.00 & 8.29 \\ \(B_{2}\) & 87.57\% & 88.51\% & 88.04\% & 91.45\% & 3.00 & 8.52 \\ \(B_{3}\) & 88.37\% & 87.27\% & 87.81\% & **92.06\%** & 3.00 & 8.89 \\ \(B_{4}\) & 88.28\% & **89.37\%** & **88.82\%** & 91.88\% & 3.01 & 9.12 \\ \(B_{5}\) & **89.01\%** & 86.98\% & 87.98\% & 91.98\% & 3.01 & 9.75 \\ \(B_{6}\) & 86.98\% & 85.31\% & 86.14\% & 88.73\% & 3.04 & 9.98 \\ \(B_{7}\) & 87.06\% & 84.93\% & 85.99\% & 88.82\% & 3.04 & 10.61 \\ \(B_{8}\) & 87.41\% & 81.10\% & 84.14\% & 86.89\% & 3.14 & 10.85 \\ \(B_{9}\) & 85.33\% & 83.24\% & 84.27\% & 86.89\% & 3.14 & 11.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative results of the YOLOv8n 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 YOLOv8n model without the motion module. Bold font indicates best values. more parameters. Similarly, the GFLOPs of the model also increase, not only due to the larger parameter count but also because the network needs to process two frames at deeper stages, further raising the computational and memory demands. To further evaluate the effectiveness of the proposed approach, we compare each configuration of the YOLOv8 variants with their corresponding counterparts enhanced using the method proposed in [29], evaluated on the test set. As previously mentioned, for both YOLOv8n and YOLOv8s, the motion module is placed after block \(B_{3}\) in the network backbone, as this configuration yields a favorable trade-off between performance and computational cost. These comparisons, shown in Table 5, provide insight into the relative effectiveness of both motion integration strategies. Table 5 shows that for both our method and approach proposed in [29], the inclusion of motion information enables the models to surpass the baseline performance, particularly in terms of Recall, F1 and mAP50. Our proposed method achives comparable results to [29] in Recall and F1 Score. Although the method from [29] achieves a higher Precision and mAP50, we will demonstrate that our approach is more robust to lighting variations-an essential factor for real world deployment, where illumination conditions can change due to shadows, cloud cover, and other environmental factors. While our model introduces a slightly higher number of GFLOPs than the method proposed in [29], it does not require any preprocessing, as motion information is integrated directly at the feature map level, allowing raw images to be used as input and contributes to greater robustness against \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Motion** & & & & & \\
**Module** & **Precision** & **Recall** & **F1 Score** & **mAP50** & **Params (M)** & **GFLOPs** \\
**Position** & & & & & & \\ \hline Baseline & 87.63\% & 84.07\% & 85.82\% & 89.79\% & 11.13 & 28.44 \\ \(B_{1}\) & 89.04\% & 87.21\% & 88.11\% & 92.03\% & 11.13 & 29.06 \\ \(B_{2}\) & **90.12\%** & 87.38\% & **88.73\%** & 92.02\% & 11.13 & 29.99 \\ \(B_{3}\) & 87.82\% & 88.27\% & 88.04\% & **92.13\%** & 11.13 & 31.45 \\ \(B_{4}\) & 86.72\% & **89.45\%** & 88.07\% & 91.62\% & 11.16 & 32.40 \\ \(B_{5}\) & 86.70\% & 88.43\% & 87.56\% & 91.94\% & 11.16 & 34.91 \\ \(B_{6}\) & 86.51\% & 85.69\% & 86.10\% & 89.31\% & 11.26 & 35.85 \\ \(B_{7}\) & 87.28\% & 86.59\% & 86.93\% & 90.47\% & 11.26 & 38.37 \\ \(B_{8}\) & 88.79\% & 84.74\% & 86.72\% & 89.75\% & 11.65 & 39.31 \\ \(B_{9}\) & 88.39\% & 84.62\% & 86.47\% & 89.81\% & 11.65 & 40.78 \\ \hline \hline \end{tabular}
\end{table}