FCOSH: A novel single-head FCOS for faster object detection in autonomous-driving systems

Abstract

In autonomous driving systems, object detection plays a pivotal role by facilitating their ability to perceive the surrounding road environment effectively. Object detection's foremost challenge pertains to its real-time operational capabilities. Achieving this necessitates reducing the detectors' computational complexity while preserving their accuracy. Nevertheless, most of the approach in object detection involves dividing image processing over multiple heads, each tasked with detecting objects at particular scales. Even though this approach improves detection accuracy, it adds an extra computational burden. In this study, our objective is to assess the feasibility of employing a single head within the originally multi-headed architecture of the FCOS detector. In response to the challenges posed by this significant modification, we propose a set of straightforward solutions, resulting in the development of a novel Fully Convolutional One-Stage with a Single Head (FCOSH) detector. Through experiments on the BDD100K benchmark, our FCOSH detector exhibits substantial improvements in computational efficiency relative to the original FCOS while concurrently achieving a superior detection 0.5% accuracy. Specifically, FCOSH achieves an 18% reduction in inference time, a 24% reduction in required FLOPs, and a 10% decrease in the number of model parameters compared to FCOS.