A Lightweight Network for UAV Multi-Scale Feature Fusion-Based Object Detection

To tackle the issues of small target sizes, missed detections, and false alarms in aerial drone imagery, alongside the constraints posed by limited hardware resources during model deployment, a streamlined object detection approach is proposed to enhance the performance of YOLOv8s. This approach introduces a new module, C2f_SEPConv, which incorporates Partial Convolution (PConv) and channel attention mechanisms (Squeeze-and-Excitation, SE), effectively replacing the previous bottleneck and minimizing both the model’s parameter count and computational demands. Modifications to the detection head allow it to perform more effectively in scenarios with small targets in aerial images. To capture multi-scale object information, a Multi-Scale Cross-Axis Attention (MSCA) mechanism is embedded within the backbone network. The neck network integrates a Multi-Scale Fusion Block (MSFB) to combine multi-level features, further boosting detection precision. Furthermore, the Focal-EIoU loss function supersedes the traditional CIoU loss function to address challenges related to the regression of small targets. Evaluations conducted on the VisDrone dataset reveal that the proposed method improves Precision, Recall, mAP0.5, and mAP0.5:0.95 by 4.4%, 5.6%, 6.4%, and 4%, respectively, compared to YOLOv8s, with a 28.3% reduction in parameters. On the DOTAv1.0 dataset, a 2.1% enhancement in mAP0.5 is observed.