Classification of the qilou (arcade building) using a robust image processing framework based on the Faster R-CNN with ResNet50

ABSTRACT

Qilou (arcade building) is a particular type of Chinese historical architecture combined with western and eastern building elements, which plays a significant role in the history of modern Chinese architecture. However, the recognition and classification of the qilou mainly rely on manual inspection, suppressing the cultural dissemination and protection of qilou relics. In this paper, we present a new framework that adopts multiple image processing algorithms and a deep learning network to automate qilou classification. First, image dataset of the qilou is enhanced based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. Then, an improved Faster R-CNN with ResNet50 (Faster R-CNN-R) is deployed for qilou image recognition. A total of 760 images captured in Guangzhou were used for training, validation, and accuracy check of the proposed framework and several contrastive networks under the same conditions. Compared to other networks, the proposed framework works better than Faster R-CNN with VGG16 (Faster R-CNN-V) and FCOS. The accuracy of the proposed framework embedded with the Faster R-CNN-R, Faster R-CNN-V, and FCOS are 80.12%, 65.17%, and 66.35%, respectively. Based on digital images captured under different lighting conditions, the proposed framework can be used to classify nine different types of qilous, with high robustness.

KEYWORDS:

• Qilou
• object detection
• Faster R-CNN
• CLAHE
• classification

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Ming Ho Li

Ming Ho Li, who just finished his master degree in Sun Yat-sen University, specializing in the structural of modern cultural architectures.

Yi Yu

Yi Yu, an Associate Professor in East China Normal University, who experts in emotional labor, care ethics, and biopolitics.

Hongni Wei

Hongni Wei, a Lecturer in Guangdong University of Foreign Studies, who specializes in culture and geography.

Ting On Chan

Ting On Chan, is an Associate Professor in Sun Yat-sen University. His research area is on the field of LiDAR and photogrammetry applications.