Deep Learning based Advanced Image Recognition in Autonomous Vehicles in Industry 4.0

The automation with integration of numerous devices are important factors in the manufacturing and production sectors in Industry 4.0. This involves the use of autonomous vehicles for diverse applications. They are based on advanced image recognition systems. The efficient functioning of the system is achieved using deep learning techniques. These networks have the ability to automatically learn from complex data. These deep learning techniques are used for the detection of obstacles, lane following and object recognition. The various constraints in obtaining the optimum performance are achieved using ant colony optimization techniques. This helps in obtaining the overall efficiency of autonomous operations. They helps in evaluating optimum solutions through iterative updation of the artificial pheromones. They are used for the optimization of hyperparameters and training parameters. They helps in fine tuning of the model for various requirements of autonomous vehicles in Industry 4.0. They results in reduced computational costs with improved convergence. The integration of deep learning with ACO helps in overcoming the challenges in the deployment of advanced image recognition systems in autonomous vehicles. They helps in improving the perception capabilities of the vehicles. These autonomous vehicles provide adaptive solutions for various processes in the manufacturing and production sectors.