Single-Stage UAV Detection and Classification with YOLOV5: Mosaic Data Augmentation and PANet

Abstract

In Drone-vs-Bird Detection Challenge in conjunction with the 4th International Workshop on Small-Drone Surveillance, Detection and Counteraction Techniques at IEEE AVSS 2021, we proposed a YOLOV5-based object detection model for small UAV detection and classification. YOLOV5 leverages PANet neck and mosaic augmentation which help in improving detection of small objects. We have combined the challenge dataset with one of the publicly available UAV air to air dataset having complex background and lighting conditions for training the model. The proposed approach achieved 0.96 Recall, $0.98 mAP_{0.5}$, and $0.71 mAP_{0.5:0.95}$ on the 10% randomly sampled dataset from the whole dataset.