SDMNet: Spatially dilated multi-scale network for object detection for drone aerial imagery

Abstract

Multi-scale object detection is a preeminent challenge in computer vision and image processing. Several deep learning models that are designed to detect various objects miss out on the detection capabilities for small objects, reducing their detection accuracies. Intending to focus on different scales, from extremely small to large-sized objects, this work proposes a Spatially Dilated Multi-Scale Network (SDMNet) architecture for UAV-based ground object detection. It proposes a Multi-scale Enhanced Effective Channel Attention mechanism to preserve the object details in the images. Additionally, the proposed model incorporates dilated convolution, sub-pixel convolution, and additional prediction heads to enhance object detection performance specifically for aerial imaging. It has been evaluated on two popular aerial image datasets, VisDrone 2019 and UAVDT, containing publicly available annotated images of ground objects captured from UAV. Different performance metrics, such as precision, recall, mAP, and detection rate, benchmark the proposed architecture with the existing object detection approaches. The experimental results demonstrate the effectiveness of the proposed model for multi-scale object detection with an average precision score of 54.2% and 98.4% for VisDrone and UAVDT datasets, respectively.