Design of robust deep learning-based object detection and classification model for autonomous driving applications

Recently, autonomous driving systems have become hot research which allows the drivers in making decisions to enhance safety, decrease traffic accidents, and move nearer toward completely autonomous cars and intelligent transportation systems. Autonomous driving systems necessitate consistent and accurate detection technique to detect objects in the real drivable environment. Though several object detection approaches have been available in the literature, a robust technique is needed for the recognition of occluded or truncated objects. Therefore, computer vision-based approaches can be used to accomplish cost-effective and robust solutions for the object detection process. In this aspect, this study focuses on the design of robust deep learning (DL)-enabled object detection and classification (RDL-ODC) model for autonomous driving systems. Primarily, preprocessing is performed to divide the images into local patches and transform them into a compatible form. In addition, the Adam optimizer-based MobileNetv2 model is applied as a feature extractor, and linear discriminant analysis (LDA) is used to reduce the dimensionality of the features. Moreover, the optimal kernel extreme learning machine (OKELM) model is employed as a classifier. To properly tune the parameters included in the KELM method, the cuckoo search optimization (CSO) algorithm is utilized, and consequently, the overall classification accuracy gets improvised, showing the novelty of the work. A wide variety of simulation takes place on benchmark dataset, and the results are investigated in terms of different evaluation metrics. The simulation result demonstrates the promising performances of the RDL-ODC technique over the advanced methods with the maximum average precision of 0.960 and minimum average miss rate of 0.192%.