Reconstruct Multiscale Features for Lightweight Small Object Detection in Remote Sensing Images

Most small objects are missed when object detection algorithms are transferred from natural images to remote sensing images. Constructing multi-scale features has been proven to be an effective approach for detecting small objects. However, existing methods for multi-scale features have two limitations: insufficient discriminative capability and sparse semantic-spatial information, which fail to fully leverage the potential of multi-scale features. To overcome these limitations, we propose the Multiscale Feature Reconstruction Network (MRN), which introduces three novel modules during feature extraction, fusion, and enhancement: the Composite Multi-scale Feature Extraction Module (CEM), Interlayer Feature Joint Module (IJM), and Spatial-Semantic Information Cross Module (SSM). First, CEM utilizes a multi-branch structure to aggregate scale information. Dilated convolution and asymmetric convolution are extensively used in the branches, which expand the receptive field and capture information of rectangular instances, respectively. Second, the IJM leverages a gating mechanism to achieve pixel-level feature enhancement for feature maps at different hierarchical depths. Finally, the SSM alleviates high-level semantic feature information imbalance through dual-branch information interaction. Furthermore, to utilize the limited computational resources, we propose a lightweight version called MRN_Lite. We evaluate MRN and MRN_Lite on three existing public datasets: AI-TOD, VEDAI, and VisDrone2019. Extensive experiments demonstrate the effectiveness of our method. In comparison experiments, both versions of the model outperform the state of the art (SOTA). And MRN_Lite has less than 50% of the FLOPs and parameters of MRN, which has comparable performance to the original version.