CCANet: A Cross-scale Context Aggregation Network for UAV object detection

With the rapid advancement of deep learning technology, Unmanned Aerial Vehicle (UAV) object detection demonstrates significant potential across various fields. However, multi-scale object variations and complex environmental interference in UAV images present considerable challenges. This paper proposes a new UAV object detection network named Cross-scale Context Aggregation Network (CCANet), which contains Multi-scale Convolution Aggregation Darknet (MCADarknet) and Cross-scale Context Aggregation Feature Pyramid Network (CCA-FPN). First, MCADarknet serves as a multi-scale feature extraction network. It employs parallel multi-scale convolutional kernels and depth-wise strip convolution techniques to expand the network’s receptive field, extracting feature maps at four different scales layer by layer. Second, to address interference in complex scenes, a Context Enhanced Fusion method enhances the interaction between adjacent features extracted by MCADarknet and higher-level features to form intermediate features. Finally, CCA-FPN employs a cross-scale fusion strategy to deeply integrate shallow, intermediate, and deep feature information, thereby enhancing object representation in complex scenarios. Experimental results indicate that CCANet performs well on three public datasets. In particular, ${mAP}_{50}$ and ${mAP}_{50-95}$ can reach 47.4% and 29.4% respectively on the VisDrone dataset. Compared to the baseline model, it achieves improvements of 6.2% and 4.3%.