Road Image Segmentation with Contrast Enhancement for Backlit Scenes
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Solution Overview
Problem
Current road detection technologies struggle to accurately segment roads in images captured under backlit conditions due to high brightness, leading to blurred roads and compromised driving safety.
Innovation Solution
A method involving image preprocessing techniques such as clipping, histogram equalization, and edge detection followed by a pre-trained road segmentation model to enhance road segmentation accuracy in backlit images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If image capture is performed under backlit conditions, then the scene brightness is high, but the road becomes blurred and cannot be accurately detected
Solution Approach 1:
The patent applies preliminary action by performing image preprocessing operations (clipping, histogram equalization, edge detection) on the backlit image before feeding it to the segmentation model. This preliminary processing enhances the road features and reduces the impact of high brightness, enabling accurate detection despite the challenging lighting conditions.
Solution Approach 2:
The patent uses an intermediary approach by introducing a pre-trained road segmentation model that has been specifically trained on enhanced images. This model acts as an intermediary that can process the preprocessed images and accurately segment roads even when the original image has high brightness, thus resolving the detection accuracy problem.
2Ease of operation
If road detection is performed on backlit images, then the overall detection process is simple, but the road segmentation precision is insufficient
Solution Approach 1:
The patent applies segmentation by dividing the image processing into distinct stages: clipping to obtain regions of interest, histogram equalization to enhance contrast, edge detection to highlight road boundaries, and finally segmentation using a pre-trained model. This segmented approach maintains operational clarity while significantly improving segmentation precision.
Solution Approach 2:
The patent utilizes parameter changes by applying histogram equalization which transforms the intensity distribution of the image. This parameter transformation enhances the contrast and makes road features more distinct, thereby improving segmentation precision without complicating the overall detection process.
Data Source
AI summary
A method for segmenting roads in images implemented in an electronic device includes obtaining a road image; obtaining a plurality of regions of interest based on the road image; generating a splicing region based on the plurality of regions of interest; and inputting the splicing region into a pre-trained road segmentation model, and obtaining a road segmentation image and a segmentation result in the road segmentation image.