Bounding Polygon Generation for Image Annotation
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Solution Overview
Problem
The process of manually annotating digital images for machine learning models is time-consuming and prone to errors, particularly when dealing with irregularly shaped objects like potatoes, which requires numerous mouse clicks and extensive labeling time, delaying computer vision projects.
Innovation Solution
A system that allows users to draw a bounding box around objects in images, which is then converted into a polygonal shape using Atrous Spatial Pyramid Pooling (ASPP) and convolutional neural networks, enabling efficient generation of labeled datasets for training AI models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual annotation of digital images is performed to create labeled datasets, then the quality and accuracy of training data for machine learning models is improved, but the time consumption and labor effort increase significantly
Solution Approach 1:
The system performs preliminary automated polygon generation from bounding boxes before manual refinement. By pre-processing images with automated object detection and polygon approximation algorithms, the system prepares initial labeled datasets that require minimal manual adjustment, significantly reducing the time-consuming manual annotation process while maintaining high quality standards
Solution Approach 2:
The system introduces an intermediary automated processing layer between raw images and final labeled datasets. This intermediary uses machine learning models to generate initial polygon annotations from bounding boxes, serving as a bridge that reduces the direct manual effort required while ensuring high-quality output through subsequent refinement steps
2Measurement precision
If complex polygonal shapes are manually drawn around irregularly shaped objects, then the precision of object boundary detection is improved, but the number of operations and time required increases
Solution Approach 1:
The system replaces the mechanical manual drawing process with automated computational algorithms. Machine learning models automatically generate polygonal shapes from bounding boxes, substituting the manual mechanical action of drawing with automated image processing that achieves comparable or superior precision without requiring multiple mouse clicks or complex manual operations
Solution Approach 2:
The system changes the parameter representation from manual vertex coordinates to automated polygon approximations. By transforming the annotation process from drawing individual vertices to generating polygonal shapes through algorithmic parameter adjustment, the system maintains high boundary precision while dramatically reducing operational complexity and time requirements
3Reliability
If extensive manual labeling is performed to train machine learning models, then the model accuracy and performance are improved, but the project completion time and delivery speed decrease
Solution Approach 1:
The system performs preliminary automated dataset generation before model training, creating large volumes of pre-labeled training data through automated polygon generation from bounding boxes. This preliminary action enables rapid model iteration and training without requiring extensive manual labeling for each experiment, thereby maintaining high model accuracy while significantly improving project completion speed
Solution Approach 2:
The system creates a composite labeling approach that combines automated polygon generation from bounding boxes with selective manual refinement. This composite method leverages the speed of automated processing for bulk data generation while incorporating manual quality control where needed, achieving both high model accuracy and rapid project delivery through the synergistic combination of automated and manual processes
Data Source
AI summary
An example system includes a first and second digital device. The first digital device may be configured to provide an interface displaying an image including a depiction of an object, place a bounding shape around the object, and crop contents of the bounding shape to create a portion. The second digital device may be configured to receive the portion, retrieve high-level features and low-level features, apply first Atrous Spatial Pyramid Pooling (ASPP) to the high-level features to aggregate the high-level features as aggregate features, concatenate results to create the aggregate features, up-sample, apply a convolution to the low-level features, concatenate the aggregate features with the low-level features after convolution to form combined features, segment the combined features to generate a polygonal shape outline along outer boundaries of the first object, and provide the first polygonal shape outline to the first digital device for display.