RBorderNet: Rider Border Collie Optimization-based Deep Convolutional Neural Network for road scene segmentation and road intersection classification

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.