Deep Learning Algorithm for Optimized Sensor Data Fusion in Fault Diagnosis and Tolerance

Abstract

Abstract Environmental perception is one of the key technologies to realize autonomous vehicles. The fault diagnosis process involves identifying the fault that occurred or the cause of the out-of-control condition. Here, the major objective is to locate problems in detection by analysing previous data or sequential patterns of data that cause failure. This study evaluates the use of deep learning for improved sensor data fusion in fault identification and tolerance using the KITTI dataset. The input video from the dataset has been transformed to frames through median filtering. Next, feature extraction is applied to a preprocessed image, resulting in the fusion of sensor data. Data fusion is then carried out utilizing an enhanced RPN (region proposal network). The enhanced RPN also has a loss function (object detection loss, bounding box loss and target classification loss), an estimate of ROI and feature extraction network (FEN). Through the use of the COOT connected blue monkey optimization (CCBMO) model, the weight of the optimally enhanced RPN is established. Next, using global non-maximum suppression with both global and local confidence, fault identification and tolerance are carried out. From the analysis, it clearly shows that proposed method accomplished better results in terms of accuracy, precision and specificity of 97.78%, 93.76% and 93.43%, respectively, when compared with various conventional models with respect to diverse performance measures.