Programs
- M. Tech. in Automotive Engineering -
- Fellowship In Interventional Pulmonology and Pulmonary Critical care - Fellowship
Publication Type : Journal Article
Source : Digital signal Processing (Elsevier)- SCI E
Url : https://www.sciencedirect.com/science/article/abs/pii/S1051200422002433
Campus : Chennai
School : School of Computing
Department : Computer Science and Engineering
Year : 2022
Abstract : Intersection is an important component in urban traffic networks, as it connects pedestrian flows and vehicles between the network links. Intersection classification plays an active role in mitigating traffic congestion, increasing the level of road safety and traffic efficiency. Various intersection classification methods are adopted to classify the intersection point in the road segment, but detecting the accurate location results from a complex task in an automatic driving system. Hence, this paper proposes a Rider Border Collie Optimization-based Deep Convolutional Neural Network (RBorderNet) for road scene segmentation intersection classification. The RBCO model is modeled by combining the Rider Optimization Algorithm (ROA) with Border Collie Optimization (BCO). Here, Fresnel Transform (FrT) is employed to detect the keyframes from the video based on the angular distance. DCNN classifier is used for classifying the intersection of road segments by considering the optimal region extracted from the segmented frames. The training of the DCNN classifier is accomplished by the RBCO algorithm. The developed RBCO-based DCNN achieved higher performance with the metrics, like accuracy, training error, precision, and recall is 94.51%, 5.49%, 96.43%, and 95.29%, respectively, by considering vehicles and traffic scenarios.
Cite this Research Publication : Dr.A.Michael, Mahesh , Dr.S.Veluchamy, "Rider Border Collie Optimization based Deep Convolution NN for Road Scene Segmentation & Road Intersection Classification", Digital signal Processing (Elsevier)- SCI E