Improved YOLOv8 algorithms for small object detection in aerial imagery

In drone aerial target detection tasks, a high proportion of small targets and complex backgrounds often lead to false positives and missed detections, resulting in low detection accuracy. To improve the accuracy of the detection of small targets, this study proposes two improved models based on YOLOv8s, named IMCMDYOLOv8ₛmall and IMCMDYOLOv8ₗarge. Each model accommodates different application scenarios. First, the network structure was optimized by removing the backbone P5 layer used to detect large targets and merging the P4, P3, and P2 layers, which are better suited for detecting medium and small targets; P3 and P2 serve as detection heads to focus more on small targets. Subsequently, the coordinate attention mechanism is integrated into the backbone's C2f, to create a C2fCA module that enhances the model' s focus on key information and secures a richer flow of gradient information. Subsequently, a multiscale attention feature fusion module was designed to merge the shallow and deep features. Finally, a Dynamic Head was introduced to unify the perception of scale, space, and tasks, further enhancing the detection capability for small targets. Experimental results on the VisDrone2019 dataset demonstrated that, compared with YOLOv8s, IMCMDYOLOv8ₛmall achieved improvements of 7. 7% and 5. 1% in email protected and email protected: 0. 95, respectively, with a 73. 0% reduction in the parameter count. The IMCMDYOLOv8ₗarge model showed even more significant improvements in these metrics, reaching 10. 8% and 7. 3%, respectively, with a 47. 7% reduction in the parameter count, displaying superior performance in small target detection tasks. The improved models not only enhanced the detection accuracy but also achieved model lightweighting, thereby proving the effectiveness of the improvement strategies and showcasing superior performance compared with other classic models.