Abstract: The objects captured by drones at high altitudes are generally small and have weak features,and they are greatly affec-ted by complex weather conditions.Object detection based on visible or infrared images often has high rates of missed detection and false detection.To address this problem,this paper proposes a dual-modal realtime object detection model DM-YOLO with reparameterization enhancement.Firstly,the visible and infrared images are effectively fused by channel concatenation,which makes efficient use of the complementary information in the dual-modal images at a very low cost.Secondly,a more efficient reparameterization module is proposed and a more powerful backbone network RepCSPDarkNet is constructed based on it,which effectively improves the feature extraction capability of the backbone network for dual-modal images.Then,a multi-level feature fusion module is proposed to enhance the multiscale feature representation of weak and small objects by fusing multi-scale feature information of weak and small objects with multi-receptive field dilated convolution and attention mechanism.Finally,the deep feature layer of the feature pyramid is removed,which reduces the model size while maintaining the detection accuracy.Experimental results on the large-scale dual-modal image dataset DroneVehicle show that,the detection accuracy of DM-YOLO is 2.45% higher than that of the baseline YOLOv5s,and is better than that of the YOLOv6 and YOLOv7 models.Furthermore,it effectively improves the accuracy and robustness of object detection under complex weather conditions,while achieving a detection speed of 82 frames per second,which can meet the requirements of realtime detection.

Key words: Reparameterization, Dual modality, Real-time object detection, Multiscale features, Attention mechanism