Vehicle Camera Object Detection Using Dynamic Grayscale Ranges
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Solution Overview
Problem
Conventional object detection systems in vehicles face challenges in accurately classifying objects due to variations in grayscale values caused by lighting and weather conditions, leading to inflexible learning and classification errors.
Innovation Solution
A device and method that uses a camera, storage, and processor to extract class confidences for each pixel, generate a distribution of grayscales, and determine a critical range for object classification, employing a segmentation model to dynamically adjust to environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed threshold value is used for class classification, then the classification process is simple and fast, but the learning becomes inflexible and classification accuracy decreases under varying lighting and weather conditions
Solution Approach 1:
The patent applies dynamics by replacing the fixed threshold value with a dynamic critical range that is generated in real-time based on the distribution of grayscale values for each class. The critical range is determined using statistical parameters (mean and standard deviation) calculated from the actual image data, allowing the classification threshold to adapt automatically to changing lighting and weather conditions while maintaining both speed and accuracy.
Solution Approach 2:
The patent changes the parameter from a fixed threshold to a dynamically calculated critical range based on statistical parameters. By computing the mean and standard deviation of grayscale values for each class and using these to define a critical range (mean ± k×standard deviation), the system adapts to environmental variations without requiring manual threshold adjustment, thus resolving the contradiction between simplicity and accuracy.
2Device complexity
If a fixed threshold value is used for object detection, then the detection system is simple to implement, but it cannot adapt to different environmental conditions such as lighting and weather changes
Solution Approach 1:
The system performs self-service by automatically generating its own critical range parameters from the image data without external intervention. The processor calculates the distribution of grayscale values, determines statistical parameters (mean and standard deviation), and generates the critical range autonomously for each class, enabling the system to adapt to environmental changes independently without requiring manual reconfiguration.
Solution Approach 2:
The patent makes the detection system dynamic by replacing static fixed thresholds with dynamically generated critical ranges. The critical range is recomputed based on the actual distribution of grayscale values in the image, allowing the system to automatically adapt to different lighting and weather conditions while maintaining relative simplicity in implementation.
3Loss of time
If conventional threshold-based classification is used, then the processing time is short, but classification errors increase due to grayscale value variations caused by lighting and weather
Solution Approach 1:
The patent changes the classification parameter from a fixed threshold to a dynamic critical range based on statistical parameters (mean and standard deviation) calculated from the image data. This allows the system to maintain short processing times while significantly improving accuracy by adapting to grayscale value variations caused by lighting and weather conditions through statistical characterization of each class.
Solution Approach 2:
The system incorporates feedback by using the actual distribution of grayscale values in the image to determine the critical range for classification. The processor analyzes the grayscale values, calculates statistical parameters, and uses these feedback results to generate the critical range, creating a closed-loop system that improves accuracy without substantially increasing processing time.
Data Source
AI summary
A device and a method for detecting an object includes a camera configured for obtaining an image frame for an external environment of a vehicle, storage configured for storing a class classification model of the image frame, and a processor. The processor extracts a class for distinguishing the object in the image frame, determines class confidences for each of pixels of the image frame, generates a distribution of confidences for grayscales of pixels belonging to a class of interest based on the class confidences, generates a critical range for determining a grayscale of the class of interest based on the distribution of the confidences for the grayscales, and generates a mask based on the critical range.