Image Processing Method of a Visual Communication System Based on Convolutional Neural Network

Abstract

Unmanned motion platforms are being used in a wide range of industries. Unmanned motion platforms must have an autonomous and intelligent navigation procedure in order to carry out their system functions. Traditional inertial navigation and radio navigation have poor accuracy and autonomy when not dependent on satellite circumstances. The accuracy of image recognition algorithms must meet strict standards. This study and exploration of the high-precision scene image recognition system is based on convolutional neural network structure optimization. To demonstrate the viability of the approach, simulation experiments are carried out on the NUC dataset using the recognition technique based on a convolutional neural network that is proposed. The fundamental network architecture of a convolutional neural network is optimized using the L2 regularization technique. The experimental findings demonstrate that the NUC dataset now has better recognition accuracy. In terms of recognition accuracy, the suggested method satisfies the predetermined requirements.

YOLO-DCNet

“…Wang et al, 2023;X. Li et al, 2023;Sun et al, 2023). For instance, within the realm of aviation and transportation system imagery applications, relevant data sets (Behera et al, 2023) and models (Jain et al, 2022) continue to be proposed by researchers.…”

Semantic-Based Optimization of Deep Learning for Efficient Real-Time Medical Image Segmentation

“…These innovations have fostered significant enhancements in the realms of data security (Li et al, 2019;Yu et al, 2018;Kaushik & Gandhi, 2022), retrieval (Wang et al, 2 2020; Nhi et al, 2022), and utilization (Yu & Reiff-Marganiec, 2022). The cumulative research in this field highlights the criticality of amalgamating these cutting-edge technologies to augment the efficiency, security, and efficacy of healthcare delivery systems (Nhi et al, 2022;Hidalgo et al, 2022;Sarivougioukas & Vagelatos, 2022;Sun et al, 2023).…”