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 of 10.45% and 2.67%, respectively, compared to YOLOv7.