|
|
|
|
|
Chuan Xu, Qi Zhang, Liye Mei, Xiufeng Chang, Zhaoyi Ye, Junjian Wang, Lang Ye and Wei Yang
Road crack detection is one of the important issues in the field of traffic safety and urban planning. Currently, road damage varies in type and scale, and often has different sizes and depths, making the detection task more challenging. To address this ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yingxiang Zhao, Lumei Zhou, Xiaoli Wang, Fan Wang and Gang Shi
Cracks are a common type of road distress. However, the traditional manual and vehicle-borne methods of detecting road cracks are inefficient, with a high rate of missed inspections. The development of unmanned aerial vehicles (UAVs) and deep learning ha...
ver más
|
|
|
|
|
|
|
|
|
|
|
Xinjian Xiang, Haibin Hu, Yi Ding, Yongping Zheng and Shanbao Wu
This study proposes a GC-YOLOv5s crack-detection network of UAVs to work out several issues, such as the low efficiency, low detection accuracy caused by shadows, occlusions and low contrast, and influences due to road noise in the classic crack-detectio...
ver más
|
|
|
|
|
|
|
|
|
|
|
Wafae Hammouch, Chaymae Chouiekh, Ghizlane Khaissidi and Mostafa Mrabti
Crack is a condition indicator of the pavement?s structure. Generally, crack detection is an essential task for effective diagnosis of the road network. Moreover, evaluation of road quality is necessary to ensure traffic security. Since 2011, a periodic ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Li Li, Baihao Fang and Jie Zhu
One of the most critical tasks for pavement maintenance and road safety is the rapid and correct identification and classification of asphalt pavement damages. Nowadays, deep learning networks have become the popular method for detecting pavement cracks,...
ver más
|
|
|
|
|
|
|
|
|
|
|
Xiaohu Zhang and Haifeng Huang
Crack detection is an important task for road maintenance. Currently, convolutional neural-network-based segmentation models with attention blocks have achieved promising results, for the reason that these models can avoid the interference of lights and ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Hui Luo, Jiamin Li, Lianming Cai and Mingquan Wu
Automatic pavement crack detection is crucial for reducing road maintenance costs and ensuring transportation safety. Although convolutional neural networks (CNNs) have been widely used in automatic pavement crack detection, they cannot adequately model ...
ver más
|
|
|
|
|
|