AVS-YOLO: Object Detection in Aerial Visual Scene

Difficult object detection and class imbalance in object detection are the two main challenges faced by aerial image object detection. Difficult objects include small objects, objects of scale variation and objects with serious background interference. Class imbalances come from the number of different classes of objects and sampling of positive and negative samples. Due to these challenges, conventional object detection models usually cannot effectively detect objects in aerial images, especially in the balance between network speed and accuracy. In this paper, the YOLOv3 network structure was improved and an object detection method under the aerial visual scene (AVS-YOLO) was proposed. By introducing a type of densely connected feature pyramid strategy, a scale-aware attention module was constructed, considering both residual dense network blocks and the median-frequency-balancing mechanism. On this basis, an algorithm with ideal speed and accuracy for object detection is obtained. To verify the effectiveness of the algorithm, AVS-YOLO and YOLOv3 were both used to test the VisDrone-DET2019 and UAVDT. The experimental results show that the AP of AVS-YOLO increases by 6.22% and 5.09% on the VisDrone2019 and UAVDT datasets, respectively, compared with YOLOv3. In addition, the AP of AVS-YOLO is 1.82% higher than that of YOLOv4 on the VisDrone2019 dataset. In terms of detection speed, AVS-YOLO can process 31.8 frames per second on a single Nvidia GTX 2080Ti GPU, compared with 44.1 frames per second for YOLOv3. Compared with the other one-stage network in the field of object detection, AVS-YOLO currently achieves the state-of-the-art performance with similar calculation amount on this dataset.