FIELD OF THE INVENTION

The present invention relates to waste management systems, and more particularly, to devices and methods that automate waste management.

BACKGROUND

Interpretation Considerations

This section describes technical field in detail and discusses problems encountered in the technical field. Therefore, statements in the section are not to be construed as prior art.

Discussion of History of the Problem

Common existing waste disposal systems include unclassified garbage collected from various places which are then manually separated at a waste disposal facility. The manual separation of solid waste brings health hazards for waste sorters as well as is less efficient, time consuming and not completely feasible due to the large quantity of waste disposed by modern households, business, and industry. To make a waste disposal system efficient, an automatic waste disposal system is needed for sorting, processing, crushing, compacting, and rinsing the waste using an identifier (e.g., barcode identifier, or the like).

In order to make this process efficient, various methods and systems have been introduced in the prior arts. U.S. patent Ser. No. 10/943,897 (Kline et al) discloses a waste material recovery and conversion center/power plant, to replace traditional trash transfer stations and landfills.

U.S. Pat. No. 7,269,516 (Brunner et al) discloses mining experiment information to identify pattern(s) from data measurement databases collected from observation.

U.S. patent Ser. No. 15/963,755 (Kumar et al) discloses a material sorting system that sorts materials utilizing a vision and/or x-ray system that implements a machine learning system in order to identify or classify each of the materials, which are then sorted into separate groups based on such an identification or classification.

U.S. patent Ser. No. 16/177,137 (Horowitz et al) discloses systems for optical material characterization of waste materials using machine learning. Further, the U.S. patent Ser. No. 16/247,449 (Parr et al) discloses a system control for a material recovery (or recycling) facility.

However, in the prior arts, dating back over many decades, there is no automated method and system for the waste management that is accurate. Therefore, there is a long-felt need for an inventive approach that can overcome the limitations associated with conventional waste management techniques. In order to solve these problems, the present invention provides an automated device, system and method for waste management that is fast and accurately reliable.

SUMMARY

The present invention discloses an artificial intelligence based method for an automatic waste management.

In a first aspect of the invention, a method for a waste management is disclosed. The method includes acquiring at least one image. Additionally, the method includes detecting at least one waste object from the at least one acquired image. Further, the method includes determining that the at least one detected waste object matches with a pre-stored waste object, identifying a type of the detected waste object using the pre-stored waste object, and displaying the type of the detected waste object based on the identification.

In one embodiment, the method further includes notifying the type of the detected waste object to a user.

In an alternative preferred embodiment, the pre-stored waste object is generated by acquiring a waste object dataset comprising a waste object with various categories, acquiring a portion of an image corresponding to the waste object from the acquired waste object dataset, training the portion of the image corresponding to the waste object using a machine learning model, and generating the pre-stored waste object based on the trained portion of the image corresponding to the waste object.

In an embodiment, detecting the at least one waste object from the at least one acquired image includes identifying the at least one waste object from the at least one acquired image, extracting the at least one identified waste object from the at least one acquired image by processing a foreground portion of the at least one acquired image and a background portion of the at least one acquired image, determining at least one feature parameter based on the extraction, analyzing a pixel or pixels corresponding to the at least one identified waste object based on the determined feature parameter, and detecting the at least one waste object from the at least one acquired image based on the analyzed pixel(s).

In yet another embodiment, identifying the type of the detected waste object using the pre-stored waste object includes determining whether multiple types of the detected waste object are detected, and performing one of: in response to determining that multiple types of the waste object is not detected, identifying the type of the detected waste object using at least one feature parameter, and in response to determining that multiple types of the waste object is detected, determining at least one feature parameter based on the at least one identified waste object, analyzing a pixel or pixels corresponding to the at least one identified waste object based on the determined feature parameter, and detecting the at least one waste object from the at least one acquired image based on the analyzed pixel(s).

In alternative embodiments, the feature parameter comprises a shape of the waste object, a color of the waste object, an intensity of the waste object.

In a second aspect of the present invention, an electronic device for an automatic waste management is disclosed. The electronic device includes a processor coupled to a memory, and an artificial intelligence based waste object categorizing engine coupled to the processor. The artificial intelligence based waste object categorizing engine is configured to acquire at least one image, detect at least one waste object from the at least one acquired image, and determine that the at least one detected waste object matches with a pre-stored waste object. The artificial intelligence based waste object categorizing engine is also configured to identify a type of the detected waste object using the pre-stored waste object, and may display the type of the detected waste object based on the identification.

