Midea Group Co., Ltd.

Yunke Tian

Thanh Huy Ha

Zhicai Ou

Yi Heng Chen

Mohamad Al Jazaery

Sathyanarayanan Muthusamy

System and method for reducing false negatives in object detection, including: extracting an object of interest from a respective image in a first set of training data that includes in distribution (ID) data. For each of the object of interest extracted from the respective image in the first set of training data: fusing the object of interest with an image from a second set of data that does not include any objects of interest to form a fused image; adding the fused image to the training data; and using the training data to train a detection model for object detection.

Method and system for reducing false positives in object detection neural networks caused by novel objects

System and method for providing visual-aided placement recommendation includes obtaining images of a rack configured to hold objects inside a chamber, placement of the plurality of objects on the rack being subject to preset constraints corresponding to characteristics of respective objects of the plurality of objects relative to physical parameters of respective locations on the rack; analyzing the images to determine whether the preset constraints have been violated by placement of objects on the rack; and in accordance with a determination that at least one preset constraint has been violated, generating a first output providing a guidance on proper placement of the first object on the rack that complies with the one or more preset constraints, in accordance with the physical characteristics of the first object relative to the physical parameters of the respective locations on the rack, taking into account of other objects already placed on the rack.

System and method for recommending object placement

A method includes: capturing one or more images of an unorganized collection of items inside a first machine; determining one or more item types of the unorganized collection of items from the one or more images, comprising: dividing a respective image in the one or more images into a respective plurality of sub-regions; performing feature detection on the respective plurality of sub-regions to obtain a respective plurality of regional feature vectors, wherein a regional feature vector for a sub-region indicates characteristics for a plurality of predefined local item features for the sub-region; generating an integrated feature vector by combining the respective plurality of regional feature vectors; and applying a plurality of binary classifiers to the integrated feature vector; and selecting a machine setting for the first machine based on the determined one or more clothes type in the unorganized collection of items.

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Santa Clara, CA, United States of America

Yunke Tian