【摘要】 针对无人机在航拍过程中容易受到恶劣环境的影响，导致航拍图像出现辨识度低、被障碍物遮挡、特征严重丢失等问题，提出了一种改进YOLOv7的无人机视角下复杂环境的目标检测算法(SSG-YOLOv7)。首先从VisDrone2019数据集和RSOD数据集中分别抽取图片进行五种环境的模拟，将VisDrone数据集扩充至12803张，RSOD数据集扩充至1320张。其次，聚类出更适合数据集的锚框尺寸。接着将3D无参注意力机制SimAM引入主干网络和特征提取模块中，增加模型的学习能力。然后重构特征提取模块SPPCSPC，融合不同尺寸池化通道提取的信息同时引入轻量级的卷积模块GhostConv，在不增加模型参数量的同时提高算法对密集多尺度目标检测精度。最后使用Soft NMS优化锚框的置信度，减少算法的误检、漏检率。实验结果表明，在复杂环境的检测任务中SSGYOLOv7检测效果优良，性能指标VisDrone＿mAP@0.5和RSOD＿mAP@0.5较YOLOv7分别提高了10.45%和2.67%。更多还原

【Abstract】 To address the challenges faced by drones during UAV(unmanned aerial vehicle) photography in adverse conditions, such as low image recognition, obstruction by obstacles, and significant feature loss, a novel algorithm named SSG-YOLOv7 was proposed to enhance object detection from the perspective of drones in complex environments. Firstly, 12803 images were augmented from the VisDrone2019 dataset, and 1320 images were augmented from the RSOD dataset to simulate five different environments. Subsequently, anchor box sizes suitable for the datasets were clustered. The 3D non-local attention mechanism SimAM was integrated into the backbone network and feature extraction module to enhance the model’s learning capabilities. Furthermore, the feature extraction module SPPCSPC was restructured to integrate information extracted from channels with different pool sizes and introduce the lightweight convolution module GhostConv, thereby improving the precision of dense multi-scale object detection without increasing the model’s parameter count. Finally, Soft NMS was employed to optimize the confidence of anchor boxes, reducing false positives and missed detections.Experimental results demonstrate that SSG-YOLOv7 exhibits superior detection performance in complex environments, with performance metrics VisDrone＿mAP@0.5 and RSOD＿mAP@0.5 showing improvements of10.45% and 2.67%, respectively, compared to YOLOv7.更多还原