Object Detection Pipeline Using Segmented Neural Networks
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Solution Overview
Problem
Conventional digital image editing systems are limited in flexibility, accuracy, and efficiency for detecting and selecting objects, often failing to identify diverse object classes, providing imprecise object masks, and requiring extensive user interaction and computing resources.
Innovation Solution
The development of an object selection system that utilizes a pipeline of neural networks to automatically detect and select objects based on natural language inputs, allowing for the identification of various object classes and attributes, and enabling precise object masking with reduced user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional object detection systems are used, then the system structure is simple, but the object detection accuracy and precision are poor
Solution Approach 1:
The system divides object detection into multiple specialized neural networks, each trained to detect specific object classes (e.g., animals, vehicles, people). This segmentation allows each network to focus on particular object types, improving detection accuracy and precision for those specific classes while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The system employs a universal object detection framework that can detect multiple object classes simultaneously through a coordinated ensemble of specialized neural networks. This multi-functional approach enables the system to handle diverse object types (animals, vehicles, people, etc.) within a unified detection pipeline, improving overall detection accuracy without requiring completely separate systems for each object class.
2Manufacturing precision
If conventional object detection systems are used, then the system is easy to operate, but the object selection precision and mask accuracy are insufficient
Solution Approach 1:
The system performs automatic object selection and mask generation without requiring manual user intervention. The specialized neural networks automatically identify objects, generate precise masks, and select target objects based on detection confidence scores and object attributes, eliminating the need for users to manually adjust selections or correct imprecise masks while achieving high mask precision.
Solution Approach 2:
The system adjusts detection parameters dynamically by selecting different specialized neural networks based on the object class being detected. Each network is optimized with specific parameters for its target object type, enabling high mask precision for different object classes while maintaining ease of operation through automatic parameter selection rather than manual configuration.
3Productivity
If conventional end-to-end neural networks are used, then the system structure is simple, but the debugging efficiency and fault localization are poor
Solution Approach 1:
The system segments the object detection process into distinct, independent neural network modules, each responsible for detecting specific object classes. This modular segmentation enables efficient debugging by allowing developers to test and optimize individual networks separately, quickly locate faults in specific object class detection, and improve overall system productivity without requiring complete system retraining.
Solution Approach 2:
The system employs a dynamic architecture where specialized neural networks can be selectively activated based on the detection task at hand. This dynamic modularity allows the system to adaptively choose which networks to engage, improving debugging efficiency by enabling targeted testing of specific object classes while maintaining the flexibility to add or remove networks as needed without restructuring the entire system.
4Speed
If conventional object detection systems are used, then the computing resources required are low, but the detection speed and efficiency for multiple object classes are poor
Solution Approach 1:
The system dynamically selects and activates only the specialized neural networks relevant to the current detection task. Instead of running all networks simultaneously, the system engages only those networks trained for specific object classes present in the image, improving detection speed for multiple object classes while optimizing computational resource usage by avoiding unnecessary processing.
Solution Approach 2:
The system segments the detection workload across multiple specialized neural networks, allowing parallel processing of different object classes. This segmentation enables the system to detect multiple object types simultaneously at high speed while distributing computational resources efficiently across specialized networks rather than overloading a single general-purpose network.
Data Source
AI summary
The systems, methods, a non-transitory computer readable mediums relate to an object selection system that accurately detects and automatically selects user-requested objects (e.g., query objects) in a digital image. For example, the object selection system builds and utilizes an object selection pipeline to determine which object detection neural network to utilize to detect a query object based on analyzing the object class of the query object. In addition, the object selection system can add, update, or replace portions of the object selection pipeline to improve overall accuracy and efficiency of automatic object selection within an image.