Intelligent Recognition Technology for the Segmentation of Traffic Indication Images Concerning Different Pavement Materials - Juanhong Xie; Guojian Shi; Weizhi Zhu (Applied Bionics and Biomechanics)
Traffic indication is an important part of the road environment, providing information about road conditions, restrictions, prohibitions, warnings, and the current status related to the flow of the traffic and other navigational aspects. The shape, color, and pictogram of a traffic indication are encoded into the visual characteristics of traffic signs. Not paying attention to these traffic signs could lead directly or indirectly to traffic accidents. In this article, the support traffic indication vector recognition (STIVR) method is proposed to classify the best signal detection to avoid traffic congestion and accidents. The proposed STIVR recognizes the traffic indication system automatically, reduces occurrences of traffic accidents, and helps drivers move safely on different pavement materials. Besides, the adaptive median filter (AMF) algorithm is used to pre-process and protect the traffic indication images without obscuring them. Thus, it indicates the edge of the non-smoothed nasty ferment from the service. In the detection of traffic events, indication images are enhanced, pre-treated, and divided according to symbols and their characteristics such as color, shape, or both. The output becomes a segmented image, including the available space identified as a road sign. The experimental results show that the proposed method functions well; achieves a sufficiently higher process speed and better segmentation of traffic indications and more accuracy in recognition of the objects. For example, the proposed method reaches a higher sensitivity performance of 96%.