Abstract: Aiming at the existing problems of multi-scale feature decoupling, insufficient representation of deformable objects, and limited retention of shallow features in UAV-based small object detection,this paper proposes an improved YOLOv8s-based detection algorithm named AC-YOLO (Accurate-YOLO). The proposed algorithm introduces a Multi-scale Dilated Convolution Residual (MDCR) mechanism into the backbone network to enhance the convolutional module and architecture. By designing parallel convolutional structures with varying dilation rates, the model simultaneously expands the receptive field and strengthens local detail perception, while reducing redundant parameters. In the feature fusion stage, the neck structure is reconstructed by integrating a lightweight architecture (Slim-neck) and an additional P2 detection layer, which effectively improves the utilization of shallow features and significantly reduces the missed detection rate. To address challenges such as complex object contours and large-scale background variations, an improved deformable convolution-based dynamic detection head Deformable Convolutional Networks v3-dynamic head, (DCNv3-dyhead) is proposed. This module learns adaptive sampling positions in key regions, enabling better perception and representation of irregular-shaped objects. In addition, the Inner-IoU concept is introduced into the construction of the Shape-IoU loss function, replacing the original loss with Inner-shapeIoU to further enhance localization accuracy and bounding box regression performance. Comparative experiments conducted on the VisDrone-2019 dataset demonstrate that the improved model achieves a 12.8 percentage point increase in mAP50, reaching 52.6%, and a 9.1 percentage point increase in mAP50-95, reaching 32.6%. On the Flow-Img dataset, the model achieves an mAP50 of 84.5%. These results indicate that the proposed model exhibits superior accuracy and strong generalization capability for small object detection tasks from UAV perspectives.

摘要： 针对无人机小目标检测中多尺度特征解耦困难、形变目标表征不足及浅层特征保留受限等问题,提出一种基于改进YOLOv8s的目标检测算法AC-YOLO（Accurate-YOLO），以更好适应无人机影像的检测需求。该算法在主干网络中引入多尺度膨胀卷积残差机制改进卷积模块及结构，通过设计不同膨胀率的并行卷积结构，在增强局部细节感知的同时拓展感受野，同时降低冗余参数。特征融合阶段结合轻量化结构与额外引入的P2检测层重构颈部网络，有效增强浅层特征的利用率，显著降低漏检率。为应对目标轮廓复杂、背景尺度变化大的情况，提出改进的可变形卷积动态检测头，通过学习关键区域的可变卷积采样位置，实现对非规则形状目标的自适应感知与表征。此外，在Shape-IoU损失函数构建中引入Inner-IoU思想，以Inner-shapeIoU替代原有损失函数，从而进一步增强目标定位精度与边界框拟合性能。实验在VisDrone-2019数据集上开展对比验证，结果显示改进模型在mAP50指标上相较基线提升12.8个百分点，达到52.6%，在mAP50-95上提升9.1个百分点至32.6%；在Flow-Img数据集上,mAP50达84.5%。上述结果表明,所提模型在无人机视角下的小目标检测任务中具备更优精度与良好泛化能力。