Improving Moving Insect Detection with Difference of Features Maps in YOLO Architecture

Insect detection under real-field conditions remains a challenging task due to factors such as lighting variations and the small size of insects that often lack sufficient visual features for reliable identification by deep learning models. These limitations become especially pronounced in lightweight architectures, which, although efficient, struggle to capture fine-grained details under suboptimal conditions, such as variable lighting conditions, shadows, small object size and occlusion. To address this, we introduce the motion module, a lightweight component designed to enhance object detection by integrating motion information directly at the feature map level within the YOLOv8 backbone. Unlike methods that rely on frame differencing and require additional preprocessing steps, our approach operates on raw input and uses only two consecutive frames. Experimental evaluations demonstrate that incorporating the motion module leads to consistent performance improvements across key metrics. For instance, on the YOLOv8n model, the motion module yields gains of up to 5.11% in mAP50 and 7.83% in Recall, with only a small computational overhead. Moreover, under simulated illumination shifts using HSV transformations, our method exhibits robustness to these variations. These results highlight the potential of the motion module as a practical and effective tool for improving insect detection in dynamic and unpredictable field scenarios.