Feature refinement with DBO: optimizing RFRC method for autonomous vehicle detection

Abstract

In today’s world, the utilization of a large number of vehicles has led to congested traffic conditions and an increase in accidents. These issues are considered primary problems in the transportation field. Therefore, there is a pressing need to develop a novel method for monitoring traffic. To address this, we propose a new model called the residual faster recurrent convolutional (RFRC) algorithm. While the proposed model achieves good detection accuracy, it must also meet the demands of real-life scenarios. In this approach, the ResNet-50 model is combined with the faster recurrent-based convolutional neural network (FRCNN) to enable the detection of autonomous vehicles. We utilize the dung beetle optimizer (DBO) with a crossover strategy for feature selection, focusing on selecting relevant features for analysis. To validate the effectiveness of the proposed RFRC method, we conduct experiments using two datasets: the KITTI dataset and the COCO2017 dataset. The evaluation of the RFRC model is performed using various measures, including f1-score, precision, recall, accuracy, and specificity, on both datasets. The proposed RFRC model outperforms both datasets and attains better results in autonomous vehicle detection.