Evaluation of Fully Convolutional One-Stage Object Detection for Drone Detection

Abhijeet Nayak, Mondher Bouazizi, Tasweer Ahmad, Artur Gonçalves, Bastien Rigault, Raghvendra Jain, Yutaka Matsuo, Helmut Prendinger

Abstract: In this paper, we present our approach for drone detection which we submitted for the Drone-Vs-Bird Detection Challenge. In our work, we used the Fully Convolutional One-Stage Object Detection (FCOS) approach tuned to detect drones. Throughout our experiments, we opted for a simple data augmentation technique to reduce the amount of False Positives (FPs). Upon observing the results of our early experiments, our technique for data augmentation incorporates adding extra samples to the training sets including the object which generated the most number of FPs, namely other flying objects, leaves and objects with sharp edges. With the newly introduced data to the training set, our results for drone detection on the validation set are as follows: AP scores of 0.16, 0.34 and 0.65 for small-sized, medium-sized and large drones respectively.