Object Detection Device Using Segmented Low-Resolution Scanning
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Solution Overview
Problem
Conventional object detection systems face excessive processing loads and insufficient accuracy, particularly in detecting obstacles for mobile objects traveling on inclined surfaces.
Innovation Solution
An object detection device that generates low-resolution images by degrading image quality, defines partial area sets in the low-resolution images, and calculates total values of feature differences between adjacent areas to extract points of interest, allowing for efficient object detection while reducing processing load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution image processing is performed on the entire captured image to ensure detection accuracy, then detection precision is improved, but processing load increases excessively
Solution Approach 1:
The captured image is divided into multiple blocks, and each block is further divided into sub-blocks. This segmentation allows the system to process only relevant regions at high resolution rather than the entire image, thereby reducing overall processing load while maintaining detection accuracy for objects of interest.
Solution Approach 2:
Different processing resolutions are applied to different regions of the image. The system performs high-resolution processing only on blocks containing objects of interest, while using lower resolution for other regions. This local quality approach ensures detection accuracy is maintained where needed while reducing processing load in less critical areas.
2Measurement precision
If the entire captured image is processed at high resolution to maintain detection accuracy, then detection precision is improved, but processing time increases
Solution Approach 1:
The image processing is segmented into multiple stages: initial low-resolution scanning of the entire image, followed by high-resolution processing only of selected blocks. This segmentation significantly reduces processing time while maintaining detection accuracy by avoiding unnecessary high-resolution processing of empty or irrelevant regions.
Solution Approach 2:
The system performs preliminary low-resolution processing of the entire image first to identify regions containing objects of interest. This preliminary action allows subsequent high-resolution processing to be focused only on relevant areas, thereby reducing overall processing time while maintaining detection accuracy.
3Productivity
If only low-resolution processing is performed to reduce processing load, then processing efficiency is improved, but detection accuracy deteriorates
Solution Approach 1:
The processing is segmented into two levels: low-resolution processing for the entire image to maintain high processing efficiency, and high-resolution processing for selected blocks to ensure detection accuracy. This segmentation allows the system to achieve both processing efficiency and detection accuracy simultaneously.
Solution Approach 2:
The system applies different processing qualities to different regions: low-resolution processing for most areas to maintain efficiency, and high-resolution processing locally for blocks containing objects of interest to ensure accuracy. This local quality differentiation resolves the contradiction between processing efficiency and detection accuracy.
4Measurement precision
If comprehensive feature analysis is performed on all image regions to ensure detection accuracy, then detection precision is improved, but device complexity increases
Solution Approach 1:
The comprehensive feature analysis is segmented and applied only to selected blocks rather than the entire image. This segmentation reduces device complexity by limiting complex processing to only necessary regions while maintaining detection accuracy through targeted analysis of blocks containing objects of interest.
Solution Approach 2:
Complex feature analysis is applied locally only to blocks identified as containing objects of interest, rather than uniformly across the entire image. This local quality approach maintains detection accuracy where needed while reducing overall device complexity by avoiding unnecessary complex processing in other regions.
Data Source
AI summary
An object detection device includes a storage medium configured to store computer-readable instructions, and a processor connected to the storage medium, in which the processor executes the computer-readable instructions to execute acquiring a captured image of a surface along which a mobile object is able to travel, which is captured with an inclination with respect to the surface, generating a low-resolution image obtained by lowering image quality of the captured image, defining a plurality of partial area sets each having partial areas in the low-resolution image, and deriving a total value obtained by totalizing differences in feature amount between the partial areas included in each of the plurality of partial area sets and partial areas in the vicinity to extract a point of interest on the basis of the total value, each of the plurality of partial area sets is defined to include a plurality of partial areas in a target area of each partial area set, and the target area is obtained by cutting out a part of the low-resolution image limited in a vertical direction so that at least a part thereof does not overlap with another partial area set in the vertical direction.