Zhongxin Jingwei, March 29, 2022 In the ADE20K international list of scene analysis and semantic segmentation technology released in March 2022, MYbank ranked first with an overall score of 0.6730, which is another excellent achievement in the authoritative list of computer vision after winning and maintaining the cityscapes semantic segmentation list last year.
ADE20K is a dataset released by the Massachusetts Institute of Technology (MIT) in 2017 that can be used for a variety of tasks such as scene perception, segmentation, and multi-object recognition, containing more than 150 different scenes indoors and outdoors, more than 20,000 photos, and contestants need to accurately segment and mark objects such as buildings, trees, people, glass windows, etc. through algorithms, and the higher the accuracy, the higher the score. Compared with the same type of cityscapes, the environment and scenario of ADE 20K setting are more complex and the challenges to algorithm adaptability are greater, and it is the authoritative benchmark dataset for semantic segmentation papers of the world's three top conferences (CVPR, ICCV and ECCV).
As an important branch of computer vision technology, scene parsing and semantic segmentation can help computers accurately perceive and understand objects on flat photos, thereby transforming unstructured images into data that can be calculated, providing a basis for restoring and understanding real-world scenes. The objects in most scenes are diverse and complex, and the color, shape, size, and posture of the same type of objects in different scenes are not the same, so how to segment and accurately identify different objects is the difficulty of such technologies.
It is understood that MYbank is currently the only institution that has maintained a leading position on both international lists, and there are also global technology companies and research institutions such as Huawei, SenseTime, Microsoft, Google, and Amazon.
The continuous development of computer vision has also brought new methods and models to financial services. As a technology bank focused on serving small and micro enterprises initiated by Ant Group, MYbank has widely applied such technologies in the fields of rural finance and small and micro financial risk control.
In the field of rural finance, MYbank took the lead in introducing satellite remote sensing image recognition technology, and carried out 10X10 meters pixel-level crop recognition through computer vision on medium and low-resolution satellite images, realizing the recognition of more than 20 kinds of staple grain crops such as corn, rice, wheat and more than ten kinds of cash crops such as apples, kiwis and citrus, and achieved 93% recognition accuracy that could only be achieved with high-resolution satellite images in the past. Based on this technology, the MYbank's Big satellite remote sensing risk control system has now covered more than 1,000 counties across the country and served hundreds of thousands of large growers.
At the same time, through the continuous optimization of the visual image recognition segmentation algorithm, it is also possible to automatically extract facilities such as farmhouses and greenhouses, and quickly identify information such as the type, quantity, and area of facilities, so as to obtain regional distribution information of rural economic activities and guide the credit level to provide better regional services.
As shown in the following figure, the basemap is a public image of the sky map area, with red (farmhouse) and green (greenhouse) marking the identified facilities respectively.
In addition, in the scene of the offline physical store, image perception technology can also be used to identify, understand and portray the industry and business behavior of the store. For example, by identifying the store front photos, shelves and even purchase and sale of contracts and invoices taken and uploaded by users, and combined with multi-dimensional cross-verification technology, online business banks can more accurately portray the industry, upstream and downstream relationships and real business appearance of small and micro operators, thus providing a basis for operating loan credit.
As shown in the following figure, by analyzing the photos, you can accurately identify the goods on the shelves of the supermarket, so as to provide a reference for the analysis of sales.
(Zhongxin Jingwei APP)