Research on a UAV-View Object-Detection Method Based on YOLOv7-Tiny

To address the issues of missed and false detections caused by small object sizes, dense object distribution, and complex scenes in drone aerial images, this study proposes a drone-view object-detection algorithm based on YOLOv7-tiny with a Partial_C_Detect detection head. The algorithm’s performance in handling object occlusion and multi-scale detection is enhanced by introducing the VarifocalLoss loss function and improving the feature fusion network to BiFPN. Furthermore, incorporating the novel Partial_C_Detect detection head and Adaptive Kernel Convolution (AKConv) improves the detection capabilities for small and dynamically changing objects. In addition, introducing the Dilated Weighted Residual (DWR) attention module optimizes the information processing flow, enhancing the algorithm’s ability to capture key information, especially in complex backgrounds. These enhancements collectively enable the model to balance high detection accuracy and computational efficiency, making it well-suited for resource-constrained UAV platforms. Experiments conducted on the VisDrone2019 dataset show that the improved algorithm achieves a [email protected] of 38.2%, with a model size of 29.01 MB and a computational complexity of 16.2 G. Compared to the original YOLOv7-tiny algorithm, the [email protected] improves by 2.9%, and the algorithm performs better in other key performance metrics, demonstrating its adaptability and robustness in drone aerial image object-detection tasks.