LightDrone-YOLO: A Novel Lightweight and Efficient Object Detection Network for Unmanned Aerial Vehicles

In recent years, the application of unmanned aerial vehicles UAVs has grown exponentially in various fields due to their convenience. These vehicles have become ubiquitous in numerous fields, including environmental monitoring, agricultural management, urban planning, traffic monitoring, and emergency rescue, playing an instrumental role in these domains. However, target detection from the perspective of a drone is fraught with challenges. These challenges include the difficulty of detecting small targets, interference from lighting and background in complex scenes, and limited hardware resources. To address these challenges, we have enhanced the YOLOv8 model and introduced a lightweight and efficient target detection model specifically designed for the perspective of a drone, named LightDrone-YOLO. Firstly, a specialised layer is incorporated into the model for the purpose of enhancing detection of small targets. Secondly, a lightweight multi-scale feature fusion neck LMFF-Neck is designed to reduce the number of parameters and computational complexity of the model and improve the fusion of multi-scale features. Thirdly, we improved the C2f module and renamed it C2f-MFEM, which is designed to enhance feature extraction. Finally, the spatial feature weighting fusion SFWF module was designed to accurately select the most valuable spatial information during the multi-scale feature fusion process. Experimental results on the Visdrone 2021 dataset demonstrate the effectiveness of the proposed method, and the mean accuracy mAP is substantially improved. In the validation and test datasets, the proposed method demonstrated superiority over other prevalent lightweight models, with mAP50 reaching 40.8 and 32.5 .