Semantic Scholar Open Access 2018 2723 sitasi

Deep Learning for Generic Object Detection: A Survey

Li Liu Wanli Ouyang Xiaogang Wang P. Fieguth Jie Chen +2 lainnya

Lihat Sumber DOI

Abstrak

Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.

Topik & Kata Kunci

Computer Science

Penulis (7)

Li Liu

Wanli Ouyang

Xiaogang Wang

P. Fieguth

Jie Chen

Xinwang Liu

M. Pietikäinen

Format Sitasi

APA MLA BibTeX

Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X. et al. (2018). Deep Learning for Generic Object Detection: A Survey. https://doi.org/10.1007/s11263-019-01247-4

Akses Cepat

Lihat di Sumber doi.org/10.1007/s11263-019-01247-4

Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Total Sitasi: 2723×
Sumber Database: Semantic Scholar
DOI: 10.1007/s11263-019-01247-4
Akses: Open Access ✓