Image Processing Produce Identification CNN Bounding Box
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Solution Overview
Problem
Text-based queries for identifying produce at product processing stations are time-consuming and consume significant power and processing resources, as they require multiple database queries and display of numerous results.
Innovation Solution
Implementing image analysis using convolutional neural networks (CNNs) to capture and process images of produce, determining bounding boxes and query image representations, which reduces database queries and conserves resources by eliminating occluded images and displaying accurate candidate items efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If text-based queries are used to identify produce, then the system can identify items without barcodes, but the process experiences high latency and consumes significant power and processing resources
Solution Approach 1:
The patent replaces the text-based query system with an image-based identification system using convolutional neural networks. Instead of requiring users to input text queries and the system to search databases, the system captures images of produce and uses CNNs to automatically identify items, substituting mechanical text processing with optical image recognition.
Solution Approach 2:
The system creates visual copies (images) of the produce items and processes these copies through neural networks for identification. The imager captures visual representations of produce, and the CNNs process these image copies to identify items, eliminating the need for text-based interaction and reducing latency.
2Reliability
If text-based queries display numerous pages of results, then comprehensive search coverage is achieved, but power and processing resources are consumed excessively
Solution Approach 1:
The system extracts only the most relevant features from produce images using CNNs, such as shape, color, and texture characteristics. Instead of displaying numerous pages of results, the system extracts key identifying features and uses them to directly identify produce, significantly reducing the information that needs to be processed and displayed.
Solution Approach 2:
The patent changes the identification parameters from text-based queries to image-based features. The CNNs transform visual information into identifying parameters, changing the fundamental approach from textual search to visual recognition, which reduces both processing requirements and power consumption while maintaining reliable identification.
3Measurement precision
If multiple database queries are performed for produce identification, then accurate results can be obtained, but processing resources and time are significantly consumed
Solution Approach 1:
The system performs preliminary actions by capturing images of produce before any identification process begins. The imager pre-captures visual data, and the CNNs pre-process this data to extract identifying features, so that when identification is needed, the system already has processed information ready, eliminating the need for multiple sequential database queries.
Solution Approach 2:
The patent merges multiple identification functions into a single image-based system. Instead of performing multiple separate database queries for different produce characteristics, the system combines shape recognition, color analysis, and texture identification into one integrated CNN process that handles all identification tasks simultaneously, improving both accuracy and efficiency.
Data Source
AI summary
A controller using image processing to identify produce is disclosed herein. The controller may include one or more memories and one or more processors communicatively coupled to the one or more memories. In some implementations, a controller may receive a trigger associated with presence of an item at a product processing zone. The controller may capture, via an imager, an image representing the item. Accordingly, the controller may apply a first convolutional neural network (CNN) to the image to determine a bounding box associated with the item. The controller may determine that the item within the bounding box satisfies an occlusion threshold and may apply a second CNN to the image to determine a query image representation. Accordingly, the controller may receive, from a database, an indication of one or more candidate items based on the query image representation and indicate, via a user interface, the candidate item(s).