FPGA Image Processing Pipelines for Real-Time Object Detection
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately detecting and classifying objects in their environment due to the computational demands of processing large amounts of sensor data, which can lead to delays and inaccuracies in motion planning.
Innovation Solution
An image processing system utilizing a Field Programmable Gate Array (FPGA) device coupled with cameras to perform image transformation and object detection, generating a multi-scale image pyramid and accessing a classification model for object identification, enabling faster and more accurate object detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional processors are used for image processing and object detection, then the system is easier to implement, but the processing speed is insufficient for real-time autonomous vehicle navigation
Solution Approach 1:
The patent replaces traditional general-purpose processors with an FPGA-based parallel processing architecture. The FPGA device implements multiple image processing pipelines that operate simultaneously, substituting the sequential mechanical processing of traditional CPUs with parallel hardware-based processing, thereby achieving real-time processing speeds required for autonomous vehicle navigation.
Solution Approach 2:
The patent divides the image processing task into multiple independent pipelines, each handling specific processing stages (image transformation, feature extraction, object detection). These pipelines process different portions of the image data simultaneously, segmenting the overall computation into parallel operations that can be executed concurrently on the FPGA device.
2Measurement precision
If more sensor data is collected to improve detection accuracy, then object classification becomes more accurate, but the computational burden increases causing processing delays
Solution Approach 1:
The patent performs preliminary image transformation and feature extraction in dedicated hardware pipelines before the main object detection process. By pre-processing the image data and extracting key features in advance using parallel FPGA operations, the system reduces the computational burden during the critical detection phase, enabling faster processing without sacrificing detection accuracy.
3Reliability
If multiple image processing pipelines are implemented for comprehensive object detection, then detection accuracy improves, but the device complexity increases
Solution Approach 1:
The patent designs the FPGA device with universal, reconfigurable logic blocks and interconnectors that can be programmed to perform multiple different processing functions. The same hardware infrastructure supports various image processing operations including transformation, feature extraction, and object detection, allowing multiple pipelines to share common resources rather than requiring dedicated hardware for each function.
Data Source
AI summary
Image processing systems can include one or more cameras configured to obtain image data, one or more memory devices configured to store a classification model that classifies image features within the image data as including or not including detected objects, and a field programmable gate array (FPGA) device coupled to the one or more cameras. The FPGA device is configured to implement one or more image processing pipelines for image transformation and object detection. The one or more image processing pipelines can generate a multi-scale image pyramid of multiple image samples having different scaling factors, identify and aggregate features within one or more of the multiple image samples having different scaling factors, access the classification model, provide the features as input to the classification model, and receive an output indicative of objects detected within the image data.