Image-Based Vehicle Detection Using Pyramid Resolution and Edge Thinning
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Solution Overview
Problem
Existing vehicle detection systems face challenges with low spatial resolution, slow scanning speed, interference among sensors, and unreliable accuracy due to varying vehicle appearances and environmental conditions, as well as the need for real-time processing of images for effective forward collision warnings.
Innovation Solution
An image-based vehicle detection and measurement system using geometry-based calculations to identify U or H shapes in images, employing pyramid resolution-reduced images and edge thinning techniques to maintain constant processing times, and distinguishing between daytime and nighttime driving conditions for accurate vehicle detection and distance measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If active sensors such as lasers, lidar, or millimeter-wave radars are used to detect distance, then distance measurement capability is improved, but spatial resolution and scanning speed deteriorate
Solution Approach 1:
The patent replaces active sensing systems (lasers, lidar, radar) with passive optical camera systems. Instead of emitting signals and measuring their reflection, the system uses image processing algorithms to detect vehicle shapes, edges, and features in captured images, thereby achieving distance measurement without the mechanical limitations of active sensors.
Solution Approach 2:
The patent creates a computational model of vehicle appearance and structure that can be matched against image data. By using template matching and geometric constraints based on known vehicle dimensions, the system can identify vehicles and estimate their distance from the camera without requiring high-resolution active sensing.
2Measurement precision
If active sensors are used for vehicle detection, then distance measurement capability is improved, but interference among sensors increases
Solution Approach 1:
The patent eliminates active sensor emission entirely by using passive optical detection. Multiple cameras can operate simultaneously without emitting signals that would interfere with each other, thus removing the interference problem inherent in active sensing systems while maintaining distance measurement capability through image processing.
3Ease of manufacture
If optical sensors are used for vehicle detection, then cost and intrusiveness are improved, but detection accuracy deteriorates due to varying vehicle appearances and environmental conditions
Solution Approach 1:
The patent employs multiple image processing parameters and algorithms that adapt to different conditions. By adjusting detection thresholds, using multiple feature extraction methods, and applying geometric constraints based on vehicle physics, the system maintains accurate detection across varying lighting, weather, and vehicle types despite using inexpensive optical sensors.
Solution Approach 2:
The patent creates a universal detection algorithm that can identify various vehicle types (cars, trucks, motorcycles) and handle different environmental conditions through a single integrated system. The algorithm uses geometric constraints and physical models that apply universally across different scenarios, making the low-cost optical sensor system equally effective in diverse conditions.
4Reliability
If large images of entire vehicles are captured for processing, then detection completeness is improved, but processing time increases beyond real-time requirements
Solution Approach 1:
The patent extracts only the essential features needed for vehicle detection and distance measurement from the full image, such as edge contours, corner points, and geometric shapes. By processing only these extracted features rather than the entire high-resolution image, the system achieves real-time processing speeds while maintaining detection accuracy.
Solution Approach 2:
The patent divides the image processing task into multiple stages: initial vehicle candidate identification, geometric feature extraction, constraint-based verification, and distance calculation. This segmentation allows each stage to process only relevant data with appropriate computational complexity, enabling real-time performance while ensuring complete and accurate detection.
Data Source
AI summary
In an example embodiment, an image based vehicle detection and measuring apparatus that employs an accelerated, structured, search method is described for quickly finding the extrema of a multivariable function. The input domain of the function is determined. The method exploits information as it becomes available, calculating a best possible value with the information available so far, and terminating further evaluation when the best possible value does not exceed the extreme value found so far. Ordering and best guess schemes are added to the best possible value idea to accelerate it further. The method is applied to speed up an image processing problem, an example of which is given, but has general application, for example, in fields such as vehicle detection.