公开(公告)号：US20220253372A1

公开(公告)日：2022-08-11

申请号：US17627090

申请日：2020-10-28

Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

Inventor： Yongkang XIE ,  Ruyue MA ,  Zhou XIN ,  Hao CAO ,  Kuan SHI ,  Yu ZHOU ,  Yashuai LI ,  En SHI ,  Zhiquan WU ,  Zihao PAN ,  Shupeng LI ,  Mingren HU ,  Tian WU

IPC: G06F11/36 ,  G06F8/35 ,  G06F8/41 ,  G06F8/20 ,  G06F9/445

Abstract: An apparatus and a method for executing a customized production line using an artificial intelligence development platform, a computing device and a computer readable storage medium are provided. The apparatus includes: a production line executor configured to generate a native form of the artificial intelligence development platform based on a file set, the native form to be sent to a client accessing the artificial intelligence development platform so as to present a native interactive page of the artificial intelligence development platform; and a standardized platform interface configured to provide an interaction channel between the production line executor and the artificial intelligence development platform. The production line executor is further configured to generate an intermediate result by executing processing logic defined in the file set and to process the intermediate result by interacting with the artificial intelligence development platform via the standardized platform interface.

公开(公告)号：US20220067375A1

公开(公告)日：2022-03-03

申请号：US17200445

申请日：2021-03-12

Applicant: Beijing Baidu Netcom Science and Technology Co., Ltd.

Inventor： Penghao ZHAO ,  Haibin ZHANG ,  Shupeng LI ,  En SHI ,  Yongkang XIE

IPC: G06K9/00 ,  G06K9/46 ,  G06T3/40 ,  G06N20/00

Abstract: A method includes: determining at least one typical object ratio from a first training data set by counting ratios of objects in training pictures of the first training data set; determining at least one picture scaling size based at least on the at least one typical object ratio; scaling the training pictures of the first training data set according to the at least one picture scaling size; obtaining a second training data set by slicing the scaled training pictures; training an object detection model using the second training data set; and performing object detection on a to-be-detected picture using the trained object detection model. The object detection method according to the embodiments of the present disclosure can be used to complete, without manual intervention, a task of detecting an extremely small object.