Service Robot Image Recognition with Selective Cropping and Prediction
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Solution Overview
Problem
Service robots face challenges in efficiently processing images for object recognition, particularly when recognition rates are low or recognition areas are small, leading to suboptimal performance in providing services.
Innovation Solution
An image processing method and apparatus that dynamically select between postprocessing and prediction processes based on recognition rates and object size, adjusting image sizes, and utilizing a processor to preprocess, recognize, and determine the quality of recognition results to optimize object recognition performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the robot performs full image processing and object recognition for all detected objects, then the recognition accuracy is improved, but the processing time and computational resource consumption increase significantly
Solution Approach 1:
The patent applies local quality by differentiating processing levels for different regions of the image. The image is divided into a first region (peripheral area) and a second region (central area), where the first region receives simplified processing (skipping full object recognition) while the second region receives comprehensive processing. This selective approach improves overall processing efficiency while maintaining recognition accuracy for important objects in the central region.
Solution Approach 2:
The patent segments the image processing task into distinct regions with different processing requirements. By dividing the image into multiple regions (first region for peripheral areas, second region for central areas) and applying different processing strategies to each segment, the system optimizes the balance between processing speed and recognition accuracy for different parts of the scene.
2Reliability
If the robot processes all objects in the image with full recognition algorithms, then the recognition completeness is improved, but the computational complexity and energy consumption increase
Solution Approach 1:
The system applies different processing qualities to different regions: the first region (peripheral) uses simplified processing to reduce computational complexity, while the second region (central) uses full recognition algorithms to maintain completeness. This localized quality adjustment resolves the contradiction by applying complexity only where necessary.
Solution Approach 2:
The patent implements dynamic processing where the recognition algorithm adapts its complexity based on the region being processed. The processor dynamically selects between simplified and comprehensive recognition approaches depending on whether the object is in the first or second region, optimizing the balance between completeness and computational complexity in real-time.
3Productivity
If the robot uses simplified processing for peripheral regions, then the processing efficiency is improved, but the recognition accuracy for small or distant objects deteriorates
Solution Approach 1:
The patent applies local quality by recognizing that different regions have different importance levels. The second region (central area) where small or distant objects are more likely to be located receives full processing with higher precision, while the first region (peripheral area) uses simplified processing. This resolves the contradiction by concentrating accuracy resources where they are most needed.
Solution Approach 2:
The system performs preliminary region classification before applying recognition algorithms. By first determining which region an object is in (first or second region), the system can pre-select the appropriate processing level, ensuring that small or distant objects in the second region receive the necessary attention before recognition attempts are made.
Data Source
AI summary
An apparatus and method for processing an image are disclosed. The method includes preprocessing an image, recognizing an object in the preprocessed image, determining whether to use a recognition result of the object based on a quality of the recognition result, selecting, in response to a determination that the recognition result is not to be used, one of a first process for postprocessing the preprocessed image and a second process for predicting the object based on a set operation mode, and operating according to the selected process.