MA-YOLO: Multi-Scale Information Prediction Network Based on the Multi-Direction Weighted Pyramid for UAV Scene

Object detection on unmanned aerial vehicles (DAVs)-captured scenarios play an essential role in several applications such as surveillance, environmental monitoring, security, disaster response strategies, and construction of transportation systems. Images captured by DAVs are all overhead vision including too many small objects, which are difficult to detect. Besides, the high-speed and low-altitude flight process of DAVs brings in the motion blur on the densely packed objects. The average, scale transformation and scene coverage are large, which brings difficulties in extracting and identifying useful information. To address these challenges, we propose a lightweight detection model named MA-YOLO. This article has made the following improvements based on YOLOv5:1)a multi-directional weighted pyramid structure (MiFPN) is proposed for fusing information of different scales and improves the ability to detect small objects.2) A learning-capable decoupling head (AD-head) is proposed to obtain small object information in a complex environment. Extensive experiments are conducted on the challenging VisDrone-DET2021 dataset to evaluate the performance of MA-YOLO. The obtained results show that the accuracy is better than other detection algorithms and the detection speed of VisDrone-DET2021 is improved from 85FPS to 109FPS. Thus, the MA-YOLO method pursues a trade-off between speed and accuracy compared to the state-of-the-art small object detection methods and ensures practicality on drones.