Object Counting via Depth Segmentation and Shape Validation
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Solution Overview
Problem
Manual counting of objects in factories and warehouses is labor-intensive, prone to errors, and ineffective in cluttered or distorted environments, especially when objects are partially visible or occluded.
Innovation Solution
An object counting system that partitions images based on depth, identifies object regions using machine learning, determines shapes, validates them against predetermined shapes, and aggregates counts to accurately count objects in a region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing image analysis methods are used to count objects, then the counting process is automated, but the accuracy deteriorates when objects are in cluttered or distorted backgrounds, partially visible, or occluded
Solution Approach 1:
The image is partitioned into multiple segments based on depth information, creating depth-based segmentations that separate objects at different distances from the camera. This segmentation allows the system to process each depth layer independently, improving detection accuracy for occluded or cluttered objects by isolating them from background interference.
Solution Approach 2:
The system transitions from traditional 2D image analysis to 3D depth-aware analysis by incorporating depth maps and spatial positioning. This dimensional enhancement allows objects to be distinguished along the depth axis, resolving ambiguities in cluttered scenes where 2D projection causes overlapping or merged detections.
2Adaptability or versatility
If manual counting is used, then flexibility and adaptability are maintained, but time consumption and labor intensity increase significantly
Solution Approach 1:
The system performs automated object detection, counting, and validation without human intervention. The depth-based segmentation and shape validation mechanisms operate autonomously to identify and count objects, eliminating the need for manual counting while maintaining adaptability through configurable parameters for different object types and environments.
3Speed
If traditional object detection is applied, then processing speed is maintained, but detection accuracy deteriorates for overlapping or merged object appearances
Solution Approach 1:
By segmenting the image into depth-based layers, the system separates overlapping objects that appear merged in 2D projection. Each depth segment is processed independently, allowing the detection algorithm to resolve individual objects even when they occlude or overlap in the image plane, thereby improving detection accuracy without significantly increasing processing time.
Solution Approach 2:
The system replaces traditional mechanical or manual object separation methods with computational depth analysis. Instead of physically separating objects or using complex mechanical positioning, the system uses depth map processing and spatial reasoning to virtually separate and identify individual objects, achieving high accuracy through information processing rather than physical manipulation.
Data Source
AI summary
Disclosed herein is a method and system for counting plurality of objects placed in a region. An image of the region is captured and partitioned into segments based on depth of the plurality of objects. Further, shape of each of the plurality of objects in each object region of each segment is determined and validated based on comparison of the determined shape with predetermined shapes. Finally, count of the plurality of objects of each shape is aggregated for determining count of the plurality of objects in the region. In an embodiment, the present disclosure helps in automatically recognizing and counting the plurality of objects of multiple dimensions and multiple shapes, even when the image of the region includes a distorted/unfavorable background.