An object detector based on visual feature region proposal

被引：0
|
作者：
Li H.-J. [1 ]
Wang H.-Y. [1 ]
Li Y. [1 ]
Ye B. [1 ]
机构：
[1] School of Information and Control Engineering, China University of Mining and Technology, Xuzhou
来源：
Kongzhi yu Juece/Control and Decision | 2020年 / 35卷 / 06期
关键词：
Convolution neural network classification; Region proposal; Target detection; Visual features extraction;
D O I：
10.13195/j.kzyjc.2018.1299
中图分类号：
学科分类号：
摘要：
Although the detector based on deep learning can achieve high detection accuracy, most of their speed cannot meet the real-time requirements. For the moment, the accuracy of the popular real-time detectors, such as single shot multibox detector (SSD) and you only look once (YOLO), is not high when detecting small objects. Therefore, a detector based on visual features region proposal is proposed, which can balance the detection accuracy and speed. This detector is divided into two parts: Region proposal and network classification. In the region proposal stage, the region of interest (ROI) is exated according to the feature information of the objects, which is also called candidate region; in the network classification stage, we use convolutional neural network (CNN) to process the ROI, then calculate class confidence of each ROI, and get the final candidates whose confidence is greater than the threshold value. Experimental results show that the detection accuracy of the proposed detector is significantly higher than that of the Faster R-CNN, SSD and YOLO, and its speed is close to the speed of the SSD and YOLO. © 2020, Editorial Office of Control and Decision. All right reserved.
引用
收藏
页码：1323 / 1328
页数：5
相关论文
共 22 条
  • [1] Xie X, Wang C, Chen S, Et al., Real-time illegal parking detection system based on deep learning, Proceedings of the 2017 International Conference on Deep Learning Technologies, pp. 23-27, (2017)
  • [2] Tsehay Y K, Lay N S, Roth H R, Et al., Convolutional neuralnetwork based deep-learning architecture for prostate cancer detection on multiparametric magnetic resonanceimages, Medical Imaging 2017, (2017)
  • [3] Zhang X, Chen G, Saruta K, Et al., Deep convolutional neural networks for all-day pedestrian detection, International Conference on Information Science and Applications, pp. 171-178, (2017)
  • [4] Sun X, Wu P, Hoi S C H, Face detection using deep learning: An improved faster RCNN approach, Neurocomputing, 299, 29, pp. 42-50, (2018)
  • [5] Jiang H, Learned-Miller E., Face detection with the faster R-CNN, Automatic Face Gesture Recognition, pp. 650-657, (2017)
  • [6] Redmon J, Divvala S, Girshick R, Et al., You only look once: Unified, real-time object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788, (2016)
  • [7] Liu W, Anguelov D, Erhan D, Et al., SSD: Single shot multibox detector, European Conference on Computer Vision, pp. 21-37, (2016)
  • [8] Lin T Y, Dollar P, Girshick R B, Et al., Feature pyramid networks for object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 936-944, (2017)
  • [9] Girshick R, Donahue J, Darrell T, Et al., Rich feature hierarchies for accurate object detection and semantic segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580-587, (2014)
  • [10] He K, Zhang X, Ren S, Spatial pyramid pooling in deep convolutional networks for visual recognition, IEEE Transactions on Pattern Analysis Machine Intelligence, 37, 9, pp. 1904-1916, (2015)
123