北京航空航天大学学报2026，Vol.52Issue(5)：1433-1444,12.DOI:10.13700/j.bh.1001-5965.2024.0135
基于自适应复合卷积的航拍小目标检测算法
Small object detection algorithm for aerial photography based on adaptive compound convolution
摘要
Abstract
To remedy the problem of aerial imagery,including the high proportion of small objects and the poor feature extraction effect,a small object detection algorithm with relatively balanced accuracy and storage resource consumption is proposed.To improve the capacity to extract fine-grained features and produce adaptable features without background information,a lightweight adaptive compound convolutional(LACC)module is first suggested.Then,a multi-scale feature fusion network based on LACC is designed to further reduce the missing rate of small targets.Secondly,the sub-branches of the spatial context pyramid(SCP)are used to replace the spatial pyramid pooling-fast(SPPF)module,which can reduce information confusion and redundancy,and adapt to small target detection scenarios.In order for the network to successfully increase the detection and positioning capabilities of occlusion-overlapping targets,a WiseIou-V3-NMS non-maximum suppression method is then built,taking into account that the detection frame contains objects but is erased.Finally,a lightweight shared convolutional GN detection head is proposed to keep the sensitivity to multi-scale feature information while reducing the number of parameters and computation.On the VisDrone2019 public dataset,the proposed algorithm achieves a mean accuracy MAP0.5 of 0.466,which is 0.077 higher than the baseline algorithm YOLOv8s,the number of network parameters is reduced by 21.6%,and the model size is reduced by 18.7%.关键词
航拍图像/小目标检测/YOLOv8/特征融合/细粒度特征Key words
aerial images/small target detection/YOLOv8/feature fusion/fine-grained features分类
航空航天引用本文复制引用
邓天民,余洋,陈月田,谢鹏飞..基于自适应复合卷积的航拍小目标检测算法[J].北京航空航天大学学报,2026,52(5):1433-1444,12.基金项目
国家重点研发计划(2022YFC3800502) （2022YFC3800502）
重庆市技术创新与应用发展专项重点项目(2022TIAD-KPX0069)National Key Research and Development Program of China(2022YFC3800502) （2022TIAD-KPX0069）
Special Key Project for Technological Innovation and Application Development in Chongqing(2022TIAD-KPX0069) （2022TIAD-KPX0069）
