Fast aircraft detection using cascaded discriminative model in photoelectric sensing system

Abstract

Aircraft detection is a fundamental problem in computer vision. As a vision-based system, the photoelectric sensing system (in airport) needs to capture the aircrafts quickly and accurately by the optical camera. Although many existing detection models reach to favorable accuracy, they are time consuming in training and testing, which is not suitable for this system. In practice, as a core part of vision-based system, detection module always occupies a lot of time in image processing and target matching. To reduce the (detection) time cost without losing detection accuracy, we designed a cascade discriminative model which includes two stages: coarse pre-detection stage and fine detection stage. In the traditional object detection models, generally, an object feature template was employed to search for all positions and levels in image pyramid with sliding window fashion. However, in our detection model, only a small number of candidate regions were pre-detected to reduce the searching space at the first stage. At the second stage, an assembled method (which includes partitioned bag-of-words method and random forest) was adopted for accelerating the feature quantization and formation. Then, the possible regions including object were decided by a non-linear SVM classifier. We evaluated our model on two benchmark databases (Caltech 101 and PASCAL 2007) and our own database (images were obtained from the optical camera), and it yields high performance. Compared with other state-of-the-art methods, our model outperforms them not only in detection speed, but also in detection accuracy.