Road Fog Detection Using CCTV and Virtual Depth
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Solution Overview
Problem
Current fog detection methods using images are inefficient and inaccurate, requiring significant network and computing resources, and are costly due to the need for depth cameras, failing to provide real-time detection and immediacy in foggy conditions, which leads to social and economic damage from traffic accidents.
Innovation Solution
A road fog detection device and method utilizing a common CCTV camera to capture images, convert fixed objects into coordinates, identify the region pattern of fog, and quickly output visual range, providing alerts based on predetermined crisis levels, effectively mimicking depth camera functionality without the need for depth cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image-based fog detection methods are used, then fog detection capability is provided, but processing speed is slow and accuracy is insufficient due to large network and computing resource requirements
Solution Approach 1:
The patent extracts only the essential features needed for fog detection (visual range, fog region patterns) from the complex image data, rather than processing the entire image. This is achieved by converting fixed objects into coordinates and identifying region patterns, which reduces computational burden while maintaining detection accuracy.
Solution Approach 2:
The patent segments the image processing task into distinct stages: capturing images with CCTV cameras, converting fixed objects to coordinates, identifying fog region patterns, and calculating visual range. This segmentation allows parallel processing and reduces the computational complexity of each individual step, thereby improving processing speed.
2Measurement precision
If depth cameras are used for fog detection, then detection accuracy is improved, but system cost increases significantly
Solution Approach 1:
The patent creates a virtual depth map by converting fixed objects in the image into coordinates and using these to reconstruct spatial information. This virtual depth representation copies the depth-camera functionality using only standard CCTV cameras, achieving similar measurement precision without the high cost of actual depth cameras.
Solution Approach 2:
The patent makes standard CCTV cameras perform multiple functions: capturing images, extracting spatial coordinates from fixed objects, and generating virtual depth information. This multi-functionality eliminates the need for specialized depth cameras, reducing system deployment costs while maintaining measurement accuracy.
3Reliability
If complex image processing algorithms are applied, then fog detection capability is enhanced, but network and computing resource consumption increases
Solution Approach 1:
The patent extracts only the critical elements (fixed objects and their coordinates, fog region patterns) from the image data, discarding redundant information. This selective extraction significantly reduces computing resource consumption while maintaining detection reliability by focusing only on the essential features needed for fog detection.
Data Source
AI summary
According to an embodiment, a device for detecting fog on a road comprises an imaging device installed to capture a two-way road and capturing a fog on the two-way road, a network configuring device provided under the imaging device and transmitting an image captured by the imaging device, a fog monitoring device receiving the image from the network configuring device, analyzing the image to thereby detect the fog, and outputting an alert per predetermined crisis level, and a display device displaying the alert output from the fog monitoring device and transmitting the alert via a wired or wireless network.