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Ayman Noor, Ziad Algrafi, Basil Alharbi, Talal H. Noor, Abdullah Alsaeedi, Reyadh Alluhaibi and Majed Alwateer
Ambulance vehicles face a challenging issue in minimizing the response time for an emergency call due to the high volume of traffic and traffic signal delays. Several research works have proposed ambulance vehicle detection approaches and techniques to p...
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Ramez M. Elmasry, Mohamed A. Abd El Ghany, Mohammed A.-M. Salem and Omar M. Fahmy
Human behavior is regarded as one of the most complex notions present nowadays, due to the large magnitude of possibilities. These behaviors and actions can be distinguished as normal and abnormal. However, abnormal behavior is a vast spectrum, so in thi...
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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...
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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...
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Son Vu Hong Pham and Khoi Van Tien Nguyen
Artificial intelligence models are currently being proposed for application in improving performance in addressing contemporary management and production issues. With the goal of automating the detection of road surface defects in transportation infrastr...
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