FPGA CNN Swapping for Adaptive Object Detection
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Solution Overview
Problem
Existing machine vision sensors lack flexibility and adaptability, leading to suboptimal performance in diverse environments and resource-constrained settings, particularly in autonomous vehicles and UAVs, due to reliance on static CNN models.
Innovation Solution
An FPGA-based system dynamically swaps between multiple CNNs for object classification, allowing sequential use of coarse and fine-grained object detection, optimizing resource usage and adapting to varying conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single static CNN model is used in traditional machine vision sensors, then the device complexity is reduced, but the adaptability and performance in diverse environments deteriorates
Solution Approach 1:
The patent implements dynamic CNN model selection and swapping capability, allowing the system to transition between different CNN models (e.g., from a first CNN to a second CNN) based on environmental conditions and detection needs. This dynamic reconfiguration enables the system to adapt to diverse environments while managing computational resources efficiently through FPGA-based implementation.
Solution Approach 2:
The patent divides the object detection task into multiple specialized CNN models, each optimized for specific detection scenarios. Instead of using one monolithic CNN for all tasks, the system segments functionality across multiple models that can be selectively activated, improving adaptability without requiring all models to run simultaneously.
2Measurement precision
If multiple CNN models are deployed simultaneously for fine-grained detection, then the detection precision is improved, but the power consumption and resource usage increases
Solution Approach 1:
The patent employs periodic or conditional CNN model swapping rather than continuous deployment of multiple models. The system switches between CNN models based on specific conditions (e.g., when fine-grained detection is needed), allowing multiple models to be available but not simultaneously active, thereby reducing power consumption while maintaining detection precision when required.
Solution Approach 2:
The system changes operational parameters by switching between different CNN models with varying complexity and resource requirements. This allows the system to adjust its detection precision and resource consumption dynamically, selecting appropriate models based on current task requirements and available resources.
3Measurement precision
If a first CNN is used for coarse detection followed by a second CNN for fine-grained detection, then the detection accuracy is improved, but the processing time increases
Solution Approach 1:
The patent implements a two-stage detection approach where a first CNN performs preliminary coarse detection to identify potential objects, and only then is a second CNN applied for fine-grained detection of specific objects of interest. This preliminary filtering action reduces the overall processing time by avoiding exhaustive fine-grained analysis of all detected objects.
Solution Approach 2:
The detection process is segmented into distinct stages with different levels of detail. The first CNN handles broad object identification while the second CNN focuses on specific fine-grained characteristics, allowing the system to achieve high accuracy for relevant objects without processing every detected object at maximum detail.
Data Source
AI summary
Systems and methods for image recognition using FPGA computer vision devices with a field-programming gate array (FPGA) and multiple convolutional neural networks (CNNs). A first CNN is used for initial object class detection. A second CNN is swapped for the first CNN for more detailed object classification and identification.