EB-Net: an efficient balanced network for accuracy and speed of remote sensing detection

Abstract

ABSTRACT Remote sensing detection is a difficult task that requires not only identifying objects with complex background, quality, and angle issues, but also being lightweight enough to be carried by edge devices. In real remote sensing scenarios, achieving accurate, fast, and low-resource-consumption automated detection remains a significant challenge. Therefore, this paper proposes an efficient balanced network (EB-Net) for real remote sensing devices. First, a dynamic sparse attention (DSA) mechanism is proposed and has been proven with high performance via the complexity analysis. In addition, a new dynamic sparse transformer (DSFormer) is constructed using DSA, which enhances feature information and adapts to image resolution by self-attention and multi-headed attention, and achieves more flexible computation by random sampling. Then, three versions of discrete distribution IoU (DDIoU) are defined for adapting various scenarios and tasks in remote sensing, and this loss function makes the model achieve high accuracy and more lightweight. Finally, to make the model more lightweight, a cropped SPPF (CroSPPF) is presented, which significantly improves the computational efficiency by the lightweight of the sequence structure and activation function. Ablation experiments are conducted on the NWPU VHR-10 dataset, and demonstrate the effectiveness of the proposed methods. Numerous comparisons with state-of-the-art detectors are conducted on the NWPU VHR-10, RSOD and DOTA datasets. The experiments show that EB-Net outperforms state-of-the-art remote sensing detection models in comprehensive performance and achieves end-to-end accurate and lightweight detection.