DREB-Net: Dual-Stream Restoration Embedding Blur-Feature Fusion Network for High-Mobility UAV Object Detection

Abstract

Object detection algorithms are pivotal components of UAV imaging systems, extensively employed in complex fields. However, images captured by high-mobility UAVs often suffer from motion blur cases, which significantly impedes the performance of advanced object detection algorithms. To address these challenges, we propose an innovative object detection algorithm specifically designed for blurry images, named dual-stream restoration embedding blur-feature fusion network (DREB-Net). First, DREB-Net addresses the particularities of blurry image object detection problem by incorporating a blurry image restoration auxiliary branch (BRAB) during the training phase. Second, it fuses the extracted shallow features via multilevel attention-guided feature fusion (MAGFF) module, to extract richer features. Here, the MAGFF module comprises local attention modules and global attention modules, which assign different weights to the branches. Then, during the inference phase, the deep feature extraction of the BRAB can be removed to reduce computational complexity and improve detection speed. In loss function, a combined loss of mean squared error (MSE) and SSIM is added to the BRAB to restore blurry images. Finally, DREB-Net introduces fast Fourier transform in the early stages of feature extraction, via a learnable frequency domain amplitude modulation module (LFAMM), to adjust feature amplitude and enhance feature processing capability. Compared to the baseline, DREB-Net achieved an approximate 7% increase in both mAP50 and mAR50 across two experimental datasets. Experimental results indicate that DREB-Net can still effectively perform object detection tasks under motion blur in captured images, showcasing excellent performance and broad application prospects. Our source code will be available at https://github.com/EEIC-Lab/DREB-Net.git.