Double FCOS: A Two-Stage Model Utilizing FCOS for Vehicle Detection in Various Remote Sensing Scenes

Abstract

Vehicle detection in various remote sensing scenes is a challenging task. Various remote sensing scenes are mixed up with images of multi-scene, multi-quality, multi-scale and multi-class. Vehicle detection models suffer from inadequate candidate boxes, weak positive proposal sampling and poor classification performance, resulting in a detection performance degradation when they are applied in various scenes. What is worse, there is no such a dataset covering various scenes which is for vehicle detection. This paper proposes a vehicle detection model called Double FCOS and a vehicle dataset called 4MVD for vehicle detection in various remote sensing scenes. Double FCOS is a two-stage detection model based on fully convolution one-stage object detection (FCOS). FCOS is exploited in the RPN stage to generate candidate boxes in various scenes. The two-stage positive and negative sample model (TPNSM) is carefully designed to enhance the positive proposal sampling effects, particularly the tiny or weak vehicles, which are ignored in FCOS. A two-step classification model (TSCM) has been designed in the RCNN stage with a proposal classification branch and point classification branch to enhance the classification performance between the various types of vehicles. 4MVD (multi-scene, multi-quality, multi-scale and multi-class vehicle dataset) is collected from various remote sensing scenes to evaluate the performance of Double FCOS. A mean average accuracy of 78.3 percentage for vehicle detection on 5 categories has been received by Double FCOS on 4MVD. Extensive experiments demonstrate that Double FCOS significantly improves the performance of vehicle detection in various remote sensing scenes.