A Novel Deep Learning Object Detection Based on PCA Features for Self Driving Cars

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In recent years, self-driving cars and reducing the number of accident casualties have drawn a lot of attention. Although it is crucial to increase driver awareness on the road, autonomous vehicles can emulate human driving and guarantee improved levels of road safety. Artificial intelligence (AI) technologies are often employed for this purpose. However, deep learning, a subset of AI, is prone to numerous errors, a wide range of threats, and needs to handle vast amounts of data, which imposes high-performance hardware requirements. This study suggests a deep learning model for object recognition that employs characteristics to describe data rather than images. Our model employs the COCO dataset as the training foundation, and it was suggested that the features be retrieved using the principal component analysis PCA extraction method. The current results demonstrate the efficacy and precision of our model, with an accuracy of 99.96 %. Furthermore, the performance indices, i.e., recall, precision, and F1-score, achieved about 1 for most of the COCO classes in training phase and promising results in testing phase.