Image Detection Device Using Segmented Frame Processing
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Solution Overview
Problem
Current image detection technologies require upgrading hardware to improve accuracy, which is costly and consumes more memory, clock frequency, and power consumption.
Innovation Solution
An image detection device and method that adjusts image sizes and divides images to generate adjusted and divided images, allowing for object detection without increasing memory usage, clock frequency, or power consumption, using a processor, storage medium, and transceiver to process and transmit images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hardware devices are upgraded to improve image detection accuracy, then detection accuracy is improved, but memory usage, clock frequency, and power consumption increase
Solution Approach 1:
The image is divided into multiple divided images (first divided image, second divided image, etc.) based on different frames. Each divided image is processed separately by the image detection module, allowing the system to detect objects at various distances without requiring the entire high-resolution image to be processed at once, thus reducing memory usage and power consumption while maintaining detection accuracy
Solution Approach 2:
The patent introduces a temporal dimension by utilizing multiple frames of the image. Instead of processing a single static image, the system divides different frames into multiple divided images and uses them as input for detection. This allows the system to leverage information across time to improve detection of far-distance objects without increasing spatial resolution requirements, thereby avoiding additional hardware resource consumption
2Measurement precision
If hardware devices are upgraded to improve image detection accuracy, then detection accuracy is improved, but device cost increases
Solution Approach 1:
The image processing is segmented into multiple stages: the image processing module divides the image into multiple divided images based on different frames, and the image detection module processes these divided images separately. This segmentation allows standard hardware to achieve enhanced detection capabilities through algorithmic processing rather than requiring expensive high-resolution camera hardware
Solution Approach 2:
The patent creates multiple copies of image data from different frames and processes them as separate divided images. By copying and processing frames differently rather than using a single high-resolution capture, the system achieves improved detection accuracy using existing hardware capabilities, avoiding the need to purchase more expensive equipment
3Measurement precision
If hardware devices are upgraded to improve image detection accuracy, then detection accuracy is improved, but memory usage increases
Solution Approach 1:
The image is divided into multiple smaller divided images from different frames. Each divided image is processed separately by the image detection module, reducing the memory footprint at any given time compared to processing a single large high-resolution image. The system only needs to hold one divided image in memory at a time for detection, significantly reducing peak memory usage
Solution Approach 2:
The system processes images in periodic frames, dividing each frame into multiple divided images and processing them sequentially. This periodic processing approach allows memory to be reused across different frames and divided images, rather than requiring all image data to be held in memory simultaneously, thus reducing overall memory requirements while maintaining detection accuracy
Data Source
AI summary
An image detection device and an image detection method are provided. The image detection method includes: obtaining an image, where the image includes an object; adjusting a first size of the image to generate an adjusted image; generating a first divided image and a second divided image according to the image; and detecting the object in the image based on a plurality of input images, where the plurality of input images includes the first divided image, the second divided image, and the adjusted image.