Enabling a safer society
Face recognition, License Plate recognition, Perimeter protection, Thermal Cameras, Security Screening & Access Control, Dahua technology is one of the world’s leaders in security technology. Dahua AI Technology is ranked #1 in the Scene Parsing Benchmark at the Massachusetts Institute of Technology (MIT – a private research university in Massachusetts USA) in April 2020.

Based on deep learning algorithms, Dahua Technology’s Scene Segmentation Technology tops the world’s best results in Scene Parsing.  Scene parsing segments and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bed.  This surpasses all major AI companies and academic research institutions, exceeding scene parsing research results in relevant papers of ICCV, ECCV, and CVPR. This achievement marks Dahua Technology’s leading position in the field of scene parsing.

World rankings
In 2018, Dahua Technology ranked first in 12 global AI rankings including road segmentation, vehicle tracking, multiple object tracking, 3D object detection, human detection, vehicle detection, scene flow, optical flow, and person re-identification.

In 2019, Dahua Technology ranked first in 10 global AI rankings including person re-identification, instance segmentation, semantic segmentation, gait recognition, and remote sensing image analysis. Dahua Technology actively accelerates the transformation of AI technology achievements into real productivity and empowers the intelligent upgrade of the industry.
Suffice it to say for those of us who are technologically challenged, that Dahua is at the forefront of security innovation.

About ADE20K_MIT
ADE20K_MIT is an evaluation dataset for MIT Scene Parsing Benchmark released and maintained by the MIT CSAIL research group. It is one of the internationally authoritative computer vision semantic segmentation algorithm evaluation datasets. The dataset is used to evaluate the performance of computer vision technologies such as Scene Perception, Scene Parsing, Instance Segmentation, and Semantic Segmentation. With a wide range of targets and a large range of target scales, the challenging evaluation attracts participants from hundreds of well-known domestic and foreign AI laboratories and top academic research institutions, including MIT, Microsoft, Peking University, Tsinghua University, Chinese Academy of Sciences, etc.