Zobrazeno 1 - 10
of 255
pro vyhledávání: '"Zhong Qu"'
Publikováno v:
CAAI Transactions on Intelligence Technology, Vol 9, Iss 2, Pp 411-424 (2024)
Abstract Automatically detecting and locating remote occlusion small objects from the images of complex traffic environments is a valuable and challenging research. Since the boundary box location is not sufficiently accurate and it is difficult to d
Externí odkaz:
https://doaj.org/article/1033f408a0894c8090a7115f37821d1e
Autor:
Sheng‐Ye Wang, Zhong Qu
Publikováno v:
IET Image Processing, Vol 17, Iss 5, Pp 1322-1333 (2023)
Abstract For the two‐stage object detector as a faster region‐convolutional neural network (Faster R‐CNN), upgrading the accuracy of object recognition depends on the proposal box, which is generated by the region proposal algorithms. Due to th
Externí odkaz:
https://doaj.org/article/8536b6b5ccac4024933afdc62368ff65
Publikováno v:
IET Image Processing, Vol 16, Iss 6, Pp 1752-1763 (2022)
Abstract The Single Shot MultiBox Detector (SSD) is one of the fastest detection algorithms. Although it has achieved good results in detection, it also has the problem of poor detection effect for small targets and occlusion between objects. Here, t
Externí odkaz:
https://doaj.org/article/032a02184cab4836b0155b38d15b3ff2
Publikováno v:
IET Image Processing, Vol 16, Iss 5, Pp 1389-1402 (2022)
Abstract Concrete surface cracks detection is an important task to ensure the safety of infrastructure. Because of the complexity of background and low contrast of concrete surface, it is difficult to detect the cracks on the concrete surface accurat
Externí odkaz:
https://doaj.org/article/5055a9261b264406bc54d0675400eede
Publikováno v:
IET Image Processing, Vol 15, Iss 9, Pp 2056-2067 (2021)
Abstract Road Tunnels are an important part of the current road transportation infrastructure. As the main form of tunnel lining diseases, cracks are easy to interact with other areas, which seriously affects the safe operation of the tunnel. Due to
Externí odkaz:
https://doaj.org/article/7084303532b84a3090fc36ee3d1f554b
Publikováno v:
IET Image Processing, Vol 15, Iss 8, Pp 1800-1813 (2021)
Abstract Keypoint‐based object detection is one of the most efficient and speedy methods at present, yet its performance is often worse than the anchor‐based method. Without prior settings in the keypoint‐based method, the huge search space of
Externí odkaz:
https://doaj.org/article/c1a23604df6848c58484809ea89d6845
Autor:
Fang Yu, Xuejing Zhu, Shuguang Yuan, Xiaojun Chen, Zheng Li, Zhong Qu, Hong Liu, Lin Sun, Fuyou Liu
Publikováno v:
Annals of Medicine, Vol 53, Iss 1, Pp 587-595 (2021)
AbstractBackground The Oxford classification of IgA nephropathy (IgAN) was revised in 2016 which lacked sufficient evidence for prognostic value of subclassification of focal segmental glomerular sclerosis (S lesion), and the proper proportion of S l
Externí odkaz:
https://doaj.org/article/1366dfeefa9847afb420440c4c62c7f3
Publikováno v:
IEEE Access, Vol 8, Pp 54564-54573 (2020)
Concrete pavement defects are an important indicator reflecting the safety status of pavement. However, it is difficult to accurately detect the concrete pavement cracks due to the complex concrete pavement environment, such as uneven illumination, d
Externí odkaz:
https://doaj.org/article/1832a2d3808747b4b959879d95a78728
Publikováno v:
IEEE Access, Vol 7, Pp 57592-57603 (2019)
Because of the impact of the variation in different concrete surface images, such as the heterogeneity of the detection environment, uneven illumination, stains, the block, and water leakage, the existing crack detection algorithms cannot detect the
Externí odkaz:
https://doaj.org/article/37d29866d4ae42ad819eaf5cba3a73ef
Publikováno v:
IEEE Access, Vol 7, Pp 57604-57615 (2019)
Edge detection is a fundamental computer vision problem and has wide applications, Convolutional neural networks (CNN) has been a good fundamental component in many image edge detection systems. But the edge detection accuracy from the detection and
Externí odkaz:
https://doaj.org/article/ae2af9244e974313b145a812650c357c