Bakhytzhan Kulambayev^1,*, Gulbakhram Beissenova^2,3, Nazbek Katayev⁴, Bayan Abduraimova⁵, Lyazzat Zhaidakbayeva², Alua Sarbassova⁶, Oxana Akhmetova⁷, Sapar Issayev⁴, Laura Suleimenova⁸, Syrym Kasenov⁶, Kunsulu Shadinova⁹, Abay Shyrakbaev¹⁰

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3403-3418, 2022, DOI:10.32604/cmc.2022.029544 - 16 June 2022

Abstract Timely detection and elimination of damage in areas with excessive vehicle loading can reduce the risk of road accidents. Currently, various methods of photo and video surveillance are used to monitor the condition of the road surface. The manual approach to evaluation and analysis of the received data can take a protracted period of time. Thus, it is necessary to improve the procedures for inspection and assessment of the condition of control objects with the help of computer vision and deep learning techniques. In this paper, we propose a model based on Mask Region-based Convolutional… More >

Qiqiang Chen^{1, *}, Xinxin Gan², Wei Huang¹, Jingjing Feng¹, H. Shim³

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2201-2215, 2020, DOI:10.32604/cmc.2020.011191 - 16 September 2020

Abstract Automatic road damage detection using image processing is an important aspect of road maintenance. It is also a challenging problem due to the inhomogeneity of road damage and complicated background in the road images. In recent years, deep convolutional neural network based methods have been used to address the challenges of road damage detection and classification. In this paper, we propose a new approach to address those challenges. This approach uses densely connected convolution networks as the backbone of the Mask R-CNN to effectively extract image feature, a feature pyramid network for combining multiple scales More >