Cascaded Refinement Neural Network for Real-Time Object Detection
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Solution Overview
Problem
Conventional object detection systems face challenges in achieving both efficiency and accuracy, particularly on mobile devices with limited processing and memory capabilities, leading to a trade-off between these two factors.
Innovation Solution
An improved neural network architecture that includes an efficient backbone network for multi-scale feature representation, a feature fusion block, a representation transfer block, and a two-step cascaded refinement workflow, which enhances processing efficiency and accuracy by retaining detailed and semantic information while alleviating the burden on the backbone network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional object detection systems use robust trained neural networks for accurate detection, then detection accuracy is improved, but processing speed and efficiency deteriorate due to limited capabilities on mobile devices
Solution Approach 1:
The system segments the object detection task into two distinct stages: a first stage that generates initial object candidate regions and classifications, and a second stage that refines these candidates. This segmentation allows each stage to be optimized differently - the first stage for speed and the second stage for accuracy - thereby resolving the contradiction between processing speed and detection accuracy on mobile devices.
Solution Approach 2:
The system introduces an intermediary component - the object candidate region generator in the first stage - that prepares refined candidate regions before they are processed by the second stage detector. This intermediary step filters and refines potential objects, reducing the burden on the second stage while maintaining high accuracy, thus enabling both speed and precision.
2Productivity
If simplified machine vision systems are used for mobile devices with limited processing capabilities, then processing efficiency is improved, but adaptability to various user scenarios and detection robustness deteriorate
Solution Approach 1:
The system employs dynamic resource allocation by implementing a two-stage architecture that can adaptively process different types of detection tasks. The first stage handles initial filtering efficiently, while the second stage provides refined detection for complex scenarios. This dynamic approach allows the system to maintain high processing efficiency while adapting to various user scenarios and detection requirements.
Solution Approach 2:
The first stage of the system performs self-service by generating refined object candidate regions and filtering out obvious non-objects before they reach the second stage. This self-service mechanism reduces the computational burden on the second stage, allowing the overall system to maintain high efficiency while preserving adaptability through the sophisticated second-stage detector.
3Measurement precision
If training datasets include thousands of images for each object classification to improve accuracy, then detection precision is improved, but training time and resource requirements worsen
Solution Approach 1:
The training process is segmented into two corresponding stages that mirror the detection architecture. The first stage model is trained on a subset of data for rapid initial learning, while the second stage model is trained on refined data for precise classification. This segmentation of training reduces the overall training time and resource requirements while maintaining high detection precision through the coordinated operation of both trained stages.
Data Source
AI summary
High resolution object detection systems and methods provide accurate, real-time, one-stage processing, and include a backbone network configured to receive an input image and generate multi-scale feature representations, a feature fusion block configured to fuse the multi-sale feature representations, a plurality of representation transfer modules configured to isolate and decouple sub-task networks and the multi-scale feature representations, and a cascade refinement module configured to process each representation transfer module output to refine predictions. The backbone network generates a plurality of image features corresponding to each of a plurality of image scales and includes a plurality of convolutional layers and a stem block after the first convolutional layer, wherein the stem block improves feature extraction performance. The feature fusion block generates feature outputs for each of a plurality of image scales.