Abstract:

Due to the small size of Unmanned Aerial Vehicles(UAVs),complex airspace background,and easy confusion with sky objects such as birds,the existing object detection models lack sufficient accuracy.Although increasing the model size can improve the detection accuracy to a certain extent,it also reduces the inferring speed and significantly increases the number of parameters and computational complexity of the model.In addition,the lack of datasets which are suitable for small UAV object detection makes it challenging to provide adequate support for designing effective models.To address the aforementioned deficiencies,this paper first constructs a dataset from existing open-source datasets using a target-area-compression based small object sample enhancement method,which can be utilized in small UAV object detection tasks.Then,we design a lightweight and high-accuracy network model called YOLO-LADC,based on the YOLOv8.This model incorporates a novel downsampling convolution structure that reduces the number of model parameters and computations while enhancing the detection accuracy.Moreover,we add a small object detection branch to the neck network of the YOLO-LADC to achieve the YOLO-LADCS,which is better suited for small UAV object detection tasks.Comparative experiments show that the YOLO-LADCS is able to improve the average accuracy of a small object by 1.1% with a 14% reduction in the number of parameters compared to the YOLOv8n(a lightweight version of the YOLOv8).

Key words: object detection, neural networks, uav detection, small object, lightweight