# Improving Moving Insect Detection with Difference of Features Maps in YOLO Architecture
## 3 Experimental Results and Discussion  
Given the objective of achieving a lightweight and efficient model, we restrict our evaluation to the YOLOv8n and YOLOv8s variants, which offer a favorable balance between performance and model size. The reported results correspond to a single training run. We observed that performance was consistent across different training sessions due to two main factors. First, the base YOLO network was always initialized with the same pretrained weights obtained from the dedicated insect dataset. Second, the newly introduced motion module was initialized using a fixed random seed. These conditions ensure consistency across training runs. Table 3 shows that, for YOLOv8n, incorporating the motion module can increase precision by up to 1.34%, with respect to baseline, when placed after block \(B_{5}\) in the network backbone. For Recall, the greatest improvement-up to 7.83%-is observed when the module is inserted after block \(B_{4}\), which also yields the highest gain in F1 Score, with an increase of 4.33%. As for mAP50, the optimal improvement of 5.11% is achieved when the module is placed after block \(B_{3}\). Based on these results, we select the configuration with the highest mAP50 improvement, as it provides also a favorable trade-off between computational overhead and detection performance, requiring only an additional 0.81 GFLOPs compared to the baseline model. We now extend this analysis to the YOLOv8s variant to assess whether similar trends hold, and to determine the most effective placement of the motion module under increased model capacity. As shown in Table 4, the greatest improvement in Precision-2.49% increased compared to the baseline model-is achieved when the motion module is inserted after block \(B_{2}\) in the network backbone. This configuration also yields the highest gain in F1 Score, with an increase of 2.91%. For Recall, the largest improvement, as in YOLOv8n, occurs when the module is placed after block \(B_{4}\), resulting in a 5.38% increase. Regarding mAP50, the best performance is again obtained when the module is placed after block \(B_{3}\), leading to a 2.34% improvement. Following the same criterion used with YOLOv8n, we select the configuration with the highest mAP50 increase-placement after block \(B_{3}\)- as it provides a favorable trade-off between performance gain and computational overhead. In this case, the model incurs an additional cost of only 3.01 GFLOPs compared to the baseline. The results above indicate that the inclusion of motion information contributes to improved detection performance. By enhancing object visibility, the motion module enables the model to better localize objects, leading to consistent gains across key evaluation metrics. Notably greater improvements are observed in YOLOv8n, suggesting that the motion module is particularly beneficial for lightweight models, where limited representational capacity can be compensated by integrating motion-based cues through the motion module. In both cases, the optimal placement for the motion module appears to lie between blocks \(B_{2}\) and \(B_{5}\) in the network backbone. As can be seen from Tables 3 and 4, for both YOLOv8n and YOLOv8s, inserting the motion module at deeper layers leads to an increase in the total number of parameters in the model. This is expected, since feature maps in later stages of the backbone have a higher number of channels, causing the \(1\times 1\) convolution in the motion module to require