基于自适应复合卷积的航拍小目标检测算法OA

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%.