Sensor Device with Threshold-Based Object Detection
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Solution Overview
Problem
Existing sensor devices face challenges in achieving accurate object detection and reducing processing loads while capturing high-quality images, as the image quality optimized for viewer aesthetics does not necessarily enhance detection accuracy, and current technologies burden processing, storage, and transfer with unnecessary data.
Innovation Solution
A sensor device with an array sensor, an image processing unit, and a threshold setting unit that adjusts parameters like exposure time, frame rate, resolution, and color gradations based on the detected object class, performing local learning to set thresholds for optimal detection accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If image quality is optimized for viewer aesthetics, then picture quality is improved, but object detection accuracy deteriorates
Solution Approach 1:
The patent applies local quality by setting different image quality parameters for different regions of the image. The region of interest (where objects to be detected are located) is processed with parameters optimized for detection accuracy, while other regions maintain aesthetic quality. This resolves the contradiction by making image quality parameter-dependent on the specific purpose of each region.
Solution Approach 2:
The patent dynamically adjusts image quality parameters based on the detected object class and region of interest. Rather than using fixed aesthetic optimization parameters, the system adapts parameters such as resolution, color depth, and compression ratio according to the specific detection requirements, thereby improving object detection accuracy without sacrificing overall image quality.
2Measurement precision
If high-resolution images are captured for object detection, then detection accuracy is improved, but processing load and data volume increase
Solution Approach 1:
The patent segments the image into a region of interest and other regions, then applies different processing and quality settings to each segment. Only the region of interest is processed at high resolution suitable for object detection, while other areas use lower resolution. This segmentation approach maintains detection accuracy for target objects while significantly reducing overall processing load and data volume.
Solution Approach 2:
The patent extracts and processes only the necessary portion of the image (region of interest) at high quality for object detection purposes. By taking out only the relevant area for detection rather than processing the entire high-resolution image, the system achieves accurate object detection with reduced computational burden and data transmission requirements.
3Measurement precision
If full image data is processed for object detection, then detection accuracy is improved, but processing time and energy consumption increase
Solution Approach 1:
The patent performs preliminary identification of the region of interest before full object detection processing. By pre-identifying where objects are likely to be located and setting appropriate regions of interest in advance, the system can focus subsequent detection processing only on these predetermined areas, thereby reducing processing time and energy consumption while maintaining detection accuracy.
Data Source
AI summary
A sensor device includes an array sensor that includes a plurality of visible light or invisible light imaging elements arranged one-dimensionally or two-dimensionally, an image processing unit that performs image processing for image signals obtained by image capturing using the array sensor, and a threshold setting unit. The threshold setting unit sets a threshold used for parameter setting for all or some of parameters used for image capturing processing associated with image capturing performed by the array sensor or image processing performed by the image processing unit to achieve a process using the parameter changed on the basis of the threshold.