Unmanned aerial vehicles (UAVs) are extensively applied in military, rescue operations, and traffic detection fields, resulting from their flexibility, low cost, and autonomous flight capabilities. However, due to the drone’s flight height and shooting angle, the objects in aerial images are smaller, denser, and more complex than those in general images, triggering an unsatisfactory target detection effect. In this paper, we propose a model for UAV detection called DoubleM-Net, which contains multi-scale spatial pyramid pooling-fast (MS-SPPF) and Multi-Path Adaptive Feature Pyramid Network (MPA-FPN). DoubleM-Net utilizes the MS-SPPF module to extract feature maps of multiple receptive field sizes. Then, the MPA-FPN module first fuses features from every two adjacent scales, followed by a level-by-level interactive fusion of features. First, using the backbone network as the feature extractor, multiple feature maps of different scale ranges are extracted from the input image. Second, the MS-SPPF uses different pooled kernels to repeat multiple pooled operations at various scales to achieve rich multi-perceptive field features. Finally, the MPA-FPN module first incorporates semantic information between each adjacent two-scale layer. The top-level features are then passed back to the bottom level-by-level, and the underlying features are enhanced, enabling interaction and integration of features at different scales. The experimental results show that the mAP50-95 ratio of DoubleM-Net on the VisDrone dataset is 27.5%, and that of Doublem-Net on the DroneVehicle dataset in RGB and Infrared mode is 55.0% and 60.4%, respectively. Our model demonstrates excellent performance in air-to-ground image detection tasks, with exceptional results in detecting small objects.
