Gate Detection for Micro Aerial Vehicles using a Single Shot Detector

Abstract

Object detection has become an essential tool in aerial robotics thanks to the use of onboard cameras in drones that enables find objects using techniques of vision. However, vision algorithms may become unreliable presenting drawback by the illumination changes. Deep learning has been used to solve tasks of classification, segmentation and detection using traditional Convolutional Neural Network (CNN) like VGG16, YOLO and AlexNet. This paper presents a gates detector system in a real-time using CNN based on a Single Shot Detector Network (SSD) for drone racing circuits. For the latter, we have adopted the SSD7 architecture to modified and present an implementation with five layers, reducing the prediction time and improve detection velocity in comparison with other architectures. For evaluation purpose, we selected three environments: simulation, indoors and outdoors to compare the prediction time, average fps and the confidence obtained in the detections of the gates.