Estimating the Distance of a Moving Object through Optimized DSP-Depthnet - G.Pundkar , Shwetambari; K. Gaikwad , Amit (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Autonomous driving requires understanding the layout of the surroundings, such as the distance to vehicles, pedestrians, and other obstacles. As the demand for enabling high-level autonomous driving has increased in recent years and visual perception is one of the critical features to enable fully autonomous driving, in this research, an efficient approach for simultaneous object detection, and depth estimation is done by the novel Hybridized Driving scene perception network (DSPNet) and Depthnet. The DSPnet is used in this research because the network uses multi-level feature maps and multi-task learning to improve the accuracy and efficiency of object detection, distance estimation, and semantic segmentation tasks from an input image. The Depthnet is the recurrent neural network architecture that is used for depth prediction, which estimates the distance between the object and the relative camera. The complexity of the distance estimation is greatly reduced in this hybridized DSP-Depth net and the performance of the distance estimation also improved. Here the Sparrow Egret Optimization (SEO) will be incorporated for the effective tuning of the hyperparameters in the network by the standard hybridization of the Sparrow Search Optimization (SSO) and Egret Swarm Optimization (ESO), where their characteristics of searching, and hunting will be used for training purposes. The proposed system achieves 99%, 97%, and 95% for accuracy, specificity, and sensitivity respectively.