Object detection for panoramic images based on MS‐RPN structure in traffic road scenes

Autor: Guofeng Tong, Huairong Chen, Yong Li, Xiance Du, Qingchun Zhang
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: IET Computer Vision, Vol 13, Iss 5, Pp 500-506 (2019)
Druh dokumentu: article
ISSN: 1751-9640
1751-9632
DOI: 10.1049/iet-cvi.2018.5304
Popis: The objection detection of panoramic image is the key part of street view, intelligent transportation, automatic driving and other technologies. Due to the shortcomings of existing algorithms in detecting panoramic images, firstly a high‐resolution panoramic image dataset is introduced, then the multi‐scale feature pyramid networks (MS‐RPN) structure is proposed and a new network with Sim‐Inception module is designed. The network can extract different scales of objects from different feature layers, so that the small object in the image can also be accurately detected. Finally, the entire detection network is trained by using the dataset constructed in this study. Meanwhile, the ROIPool is replaced by ROIAlign and the loss function is adjusted according to the network structure. The experimental results show that the detection performance on the panoramic dataset is significantly improved by authors’ proposed algorithm, which is better than other deep learning algorithms, especially for small object in the image.
Databáze: Directory of Open Access Journals
Externí odkaz: