A Particle Swarm Optimization Based Deep Learning Model for Vehicle Classification

Image classification is a core field in the research area of image processing and computer vision which vehicle critical domain. The purpose categorization to formulate compact system assist real-world problems applications such as security, traffic analysis, self-driving autonomous vehicles. recent revolution machine learning artificial intelligence has provided an immense amount support for related overtaken conventional, handcrafted means solving analysis problems. In this paper, combination pre-trained CNN GoogleNet nature-inspired problem optimization scheme, particle swarm (PSO), was employed classification. model trained on dataset obtained from Kaggle that been suitably augmented. classified using several classifiers; however, Cubic SVM (CSVM) classifier found outperform others both time consumption accuracy (94.8%). results empirical evaluations statistical tests reveal itself shown other models not only terms (94.8%) but also training (82.7 s) speed prediction (380 obs/sec).