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.
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All authors agreed on the content of the study. RK, MMYD, SM, and RS collected all the data for analysis. MMYD agreed on the methodology. RK, MMYD, SM, and RS completed the analysis based on agreed steps. Results and conclusions are discussed and written together. The author read and approved the final manuscript.
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Kannamma, R., Devi, M.M.Y., Madhusudhanan, S. et al. Feature refinement with DBO: optimizing RFRC method for autonomous vehicle detection. Intel Serv Robotics (2024). https://doi.org/10.1007/s11370-024-00520-x
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DOI: https://doi.org/10.1007/s11370-024-00520-x