Abstract
As a pivotal task within computer vision, object detection finds application across a diverse spectrum of industrial scenarios. The advent of deep learning technologies has significantly elevated the accuracy of object detectors designed for general-purpose applications. Nevertheless, in contrast to conventional terrestrial environments, remote sensing object detection scenarios pose formidable challenges, including intricate and diverse backgrounds, fluctuating object scales, and pronounced interference from background noise, rendering remote sensing object detection an enduringly demanding task. In addition, despite the superior detection performance of deep learning-based object detection networks compared to traditional counterparts, their substantial parameter and computational demands curtail their feasibility for deployment on mobile devices equipped with low-power processors. In response to the aforementioned challenges, this paper introduces an enhanced lightweight remote sensing object detection network, denoted as YOLO-Faster, built upon the foundation of YOLOv5. Firstly, the lightweight design and inference speed of the object detection network is augmented by incorporating the lightweight network as the foundational network within YOLOv5, satisfying the demand for real-time detection on mobile devices. Moreover, to tackle the issue of detecting objects of different scales in large and complex backgrounds, an adaptive multiscale feature fusion network is introduced, which dynamically adjusts the large receptive field to capture dependencies among objects of different scales, enabling better modeling of object detection scenarios in remote sensing scenes. At last, the robustness of the object detection network under background noise is enhanced through incorporating a decoupled detection head that separates the classification and regression processes of the detection network. The results obtained from the public remote sensing object detection dataset DOTA show that the proposed method has a mean average precision of 71.4% and a detection speed of 38 frames per second.