Camera Neural Network Processing Pipelining
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Solution Overview
Problem
Existing camera systems face challenges in processing high-resolution images for code reading due to the computational load required for neural networks, which exceeds the capabilities of embedded hardware, leading to errors and inefficiencies, especially in real-time applications with limited time windows.
Innovation Solution
Implementing a neural network on multiple processing units within the camera system, where the first processing unit processes image data with the first layer of the neural network during image readout, distributing the processing load and reducing data transfer requirements, while the second processing unit handles further layers and classical decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are implemented for code detection in high-resolution images, then detection accuracy is improved, but computational load exceeds embedded hardware capabilities
Solution Approach 1:
The patent segments the neural network processing into multiple layers distributed across different processing units. The first processing unit handles image data reading and initial neural network layers, while the second processing unit handles subsequent layers and classical decoding. This segmentation reduces the computational burden on any single embedded hardware unit while maintaining overall detection accuracy.
Solution Approach 2:
The patent introduces a temporal dimension to the processing architecture by enabling overlapping execution of image readout and neural network processing. The first processing unit reads image data while simultaneously processing it through neural network layers, transforming the traditional sequential processing into a parallel spatiotemporal operation that maximizes hardware utilization without increasing peak computational load.
2Speed
If high-resolution images are processed in real-time, then processing speed is improved, but data transfer bandwidth requirements exceed available capacity
Solution Approach 1:
The patent extracts and processes image data directly from the image sensor through the first processing unit without requiring complete transfer to a central processing unit. By implementing neural network layers on the first processing unit that interfaces directly with the image sensor, the system processes data in-place, eliminating the need for high-bandwidth data transfer of complete high-resolution images.
Solution Approach 2:
The patent performs preliminary neural network processing on image data as it is being read out from the sensor. The first processing unit begins applying neural network layers to incoming image data before the complete image frame is fully transferred, performing preprocessing actions that reduce the volume of data requiring subsequent transfer and processing.
3Measurement precision
If complete images are processed before next image acquisition, then processing accuracy is improved, but time window for processing is insufficient
Solution Approach 1:
The patent implements continuous useful action by overlapping the image readout process with neural network processing. While the first processing unit continues reading image data from the sensor, it simultaneously processes previously read data through neural network layers. This continuous overlapping execution ensures that processing never idle-waits for complete image acquisition, maximizing the utilization of the available time window while maintaining processing accuracy through sufficient computational depth.
Solution Approach 2:
The patent performs preliminary processing of image data through neural network layers before the complete image frame is acquired. By starting processing on partially available data and continuing as more data arrives, the system effectively extends the processing time window and prevents time loss that would occur by waiting for complete image acquisition before beginning processing.
4Power
If specialized hardware like GPUs or NPUs are used, then neural network processing capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent implements multi-functionality by designing processing units that can perform both traditional image processing tasks and neural network operations. The first and second processing units are configured to handle image data reading, neural network layer processing, and classical decoding functions, eliminating the need for separate specialized hardware like GPUs or NPUs while maintaining neural network processing capability.
Solution Approach 2:
The patent merges neural network processing functionality into the existing image processing pipeline by integrating neural network layers into the first and second processing units. This consolidation combines what would traditionally require separate specialized hardware into a unified processing architecture, reducing overall device complexity while preserving the required neural network processing power.
Data Source
AI summary
A camera (10) comprisingan image sensor (24) for capturing image data with a plurality of pixels,a first processing unit (30) for reading out the image data of the image sensor (24) in an image data stream comprising groups of adjacent pixels,a second processing unit (32) for processing the image data, and a memory (34) for storing at least one of the image data and processing results from the image data,wherein a neural network (38) is implemented on at least one of the first processing unit (30) and the second processing unit (32)and wherein the first processing unit (30) is configured to already process groups of pixels with at least a first layer (42) of the neural network while further groups of the image data are still being read out from the image sensor (24).