Abstract
In this paper, the image data of GF-2 is used. According to the results of vehicle detection using improved double threshold methods, it is found that there is a large false detection, and the false detection is mainly includes roadside shrub and lane line. Therefore, in order to enhance the accuracy of vehicle detection, this paper proposes to extract the lane line which is easy to be false detection by using the position feature and the edge feature. At the same time, the edge of the road is extracted by the location feature of shrub, and then the different texture feature between the false detection targets and the real targets are extracted. The fuzzy neural networks classifier is used to detect the vehicle targets in the sensitive area. The result shows that the accurate detection of the sensitive area and the use of the fuzzy neural networks can greatly reduce the false detection of the vehicle targets and enhance the accuracy of the double threshold vehicle detection method.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
