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

Vehicle detection in various remote sensing scenes is a challenging task. Various remote sensing scenes are mixed up with images of multiscene, multiquality, multiscale, and multiclass. 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 article proposes a vehicle detection model called double fully convolution one-stage object detection (FCOS) and a vehicle dataset called multiscene, multiquality, multiscale, and multiclass vehicle dataset (4MVD) for vehicle detection in various remote sensing scenes. Double FCOS is a two-stage detection model based on FCOS. FCOS is exploited in the RPN stage to generate candidate boxes in various scenes. The two-stage positive and negative sample model 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 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 is collected from various remote sensing scenes to evaluate the performance of double FCOS. A mean average accuracy of 78.3% for vehicle detection on five 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.