AS-YOLOR: An Improved YOLO Model for Small Object Detection in Aerial Images
TL;DR
An Adaptive and Scalable YOLO model named AS-YOLOR (Adaptive and Scalable YOLO for Rotated object detection), based on the YOLOv8 baseline is proposed, providing a solution with strong practical potential for achieving efficient and high-precision detection of small, rotated objects.
Abstract
Accurate detection of small, rotated objects in practical scenarios such as remote sensing imagery and autonomous driving remains a critical challenge. Existing methods often struggle to balance detection accuracy and real-time performance, and face limitations in capturing subtle features, fusing multi-scale information, and regressing oriented bounding box angles. To address these issues, this paper proposes an Adaptive and Scalable YOLO model named AS-YOLOR (Adaptive and Scalable YOLO for Rotated object detection), based on the YOLOv8 baseline. The model introduces systematic innovations at three levels: feature enhancement, attention mechanism, and feature fusion. Firstly, a high-resolution detection head is introduced, combined with a Cross-layer Soft-weighted Feature Fusion (CSFF) strategy, to adaptively fuse shallow detail information with deep semantic features, thereby significantly improving the model’s ability to capture features of tiny objects. Secondly, a Hybrid Attention Module (HAM), enhanced with global context, is designed. This module integrates spatial attention in parallel with an improved Global Context Aware Block, effectively strengthening the model’s focus on and discriminative power for key target regions. Finally, an Adaptive Feature Fusion Path Network (AFFPN) is constructed. By incorporating learnable weights and scale-sequence modeling, it achieves superior modeling of the nonlinear relationships between multi-scale features. Experimental results on the DOTA-V1.0 public dataset demonstrate that the proposed AS-YOLOR model achieves 82.72% on the core evaluation metric mAP@50, representing a significant improvement of 3.22 percentage points over the baseline YOLOv8 model, and outperforming various mainstream rotated object detection methods. Comprehensive ablation studies validate the effectiveness of each improved component within the model. This research provides a solution with strong practical potential for achieving efficient and high-precision detection of small, rotated objects.