# USD-YOLO: An Enhanced YOLO Algorithm for Small Object Detection in Unmanned Systems Perception
## 1 Introduction  
Object detection technology is crucial in the perception field of unmanned systems. It has been widely applied across various platforms, including unmanned ground vehicles (UGVs), unmanned aerial vehicles (UAVs), and unmanned surface vehicles (USVs) [1, 2, 3]. The detections in camera or radar images can provide necessary assistance for unmanned system perception [4]. Currently, small object detection in unmanned systems faces significant challenges due to the limitations of visual sensor performance and the complexity of real-world scenarios. For example, UGVs identify distant pedestrians and traffic signs [5], USVs detect small boats and floating objects on water surfaces [6], and UAVs recognize objects in aerial images [7]. These challenges arise from the hardware limitations of visual sensors, which cause objects to appear small or low-resolution in images, as well as from complex scenarios thatlead to densely distributed or occluded objects [8]. For general object detection algorithms in existing research, on the one hand, the small size of objects results in their features being distorted or lost during downsampling operations, while low resolution causes a mismatch between the receptive field and the size of small objects [8], leading to lower detection precision for small objects compared with larger ones. On the other hand, dense distributions and occlusions interfere with bounding boxes, causing significant fluctuations in the Intersection over Union (IoU) metric, further reducing the detection precision of small objects [9]. These issues weaken the perception capabilities of unmanned systems, directly limiting their overall performance and application scope [10]. Therefore, improving detection precision and candidate box precision for small objects in resource-constrained unmanned systems has become an urgent problem. To address this, a novel USD-YOLO algorithm is proposed to enhance detection precision. The framework of this study is illustrated in Figure 1, and the main contributions are summarized as follows:  
* In the post-processing, an innovative Anchor-Free Precision Enhancer (APE) module was designed. It dynamically adjusts box scores based on Enhanced IoU (EIoU). Unlike traditional Non-Maximum Suppression (NMS) or Soft-NMS, APE significantly improves precision for dense and small objects. * In the backbone network, the Spatial and Channel Reconstruction Convolution (SCConv) module was introduced. Compared with conventional convolution methods, SCConv reduces both spatial and channel redundancy in Convolutional Neural Network (CNN) features, enhancing feature extraction for small objects. * In the neck section, a novel C2f-Global Attention Mechanism (CGAM) module was developed. By expanding the receptive field and capturing more contextual information, CGAM outperforms existing neck designs such as PANet and BiFPN in small object detection. To demonstrate the significance of these contributions, experiments were conducted on three small object datasets commonly used in unmanned systems studies. Compared with the baseline YOLOv8 on the Citypersons dataset, USD-YOLO achieved improvements  
Figure 1: Research framework. of 8.5%, 5.9%, and 2.3% in mAP50-95, mAP50, and Recall, respectively. On Flow-Img and DOTA-v1.0 datasets, USD-YOLO improved mAP50-95 by 2.5% on both. In addition, a visualization-based comparison of results and a real UGV deployment experiment further validate the superiority and generalization capability of USD-YOLO. While the proposed enhancements significantly improve small object detection precision, they come at the cost of a slight reduction in computational speed. Compared with the original YOLOv8 baseline, the frame rate decreases by 3.5, 1.4, and 6.2 Frames Per Second (FPS) on the Citypersons, Flow-Img, and DOTA-v1.0 datasets, respectively. However, this trade-off remains acceptable for detection tasks in unmanned systems. The remainder of this paper is organized as follows. Section 2 reviews related studies on object enhancement methods and detection methods.