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Yiming Mo, Lei Wang, Wenqing Hong, Congzhen Chu, Peigen Li and Haiting Xia
The intrusion of foreign objects on airport runways during aircraft takeoff and landing poses a significant safety threat to air transportation. Small-scale Foreign Object Debris (FOD) cannot be ruled out on time by traditional manual inspection, and the...
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Noor Ul Ain Tahir, Zuping Zhang, Muhammad Asim, Junhong Chen and Mohammed ELAffendi
Enhancing the environmental perception of autonomous vehicles (AVs) in intelligent transportation systems requires computer vision technology to be effective in detecting objects and obstacles, particularly in adverse weather conditions. Adverse weather ...
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Adil Redaoui, Amina Belalia and Kamel Belloulata
Deep network-based hashing has gained significant popularity in recent years, particularly in the field of image retrieval. However, most existing methods only focus on extracting semantic information from the final layer, disregarding valuable structura...
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Shaoyan Zuo, Dazhi Wang, Xiao Wang, Liujia Suo, Shuaiwu Liu, Yongqing Zhao and Dewang Liu
In this study, a deep learning network for extracting spatial-temporal features is proposed to estimate significant wave height (????
H
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) and wave period (????
T
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) from X-band marine radar images. Since the shore-based radar image in this study is in...
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Adi Wibowo, Joga Dharma Setiawan, Hadha Afrisal, Anak Agung Sagung Manik Mahachandra Jayanti Mertha, Sigit Puji Santosa, Kuncoro Budhi Wisnu, Ambar Mardiyoto, Henri Nurrakhman, Boyi Kartiwa and Wahyu Caesarendra
Human eyes generally perform product defect inspection in Indonesian industrial production lines; resulting in low efficiency and a high margin of error due to eye tiredness. Automated quality assessment systems for mass production can utilize deep learn...
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