Intelligent Detection and Search Model for Communication Signals Based on Deep-Re-Hash Retrieval Technology

Abstract: With the explosive growth of image data, traditional image retrieval methods face challenges of low efficiency and insufficient accuracy. In view of this, the study first analyzed the traditional Deep-Re-Hash detection technology and constructed a general hash detection model. Secondly, Cauchy functions and Hadamard matrices were introduced to optimize the generation of hash centers, and an improved Deep-Re-Hash detection model was proposed. The experimental results showed that the highest precision of the improved Deep-Re-Hash was 97%, and the highest MAP value was 90%. In simulation testing, the lowest detection similarity of the improved Deep-Re-Hash detection model was 64.8%, and the detection speed at this time was 7.6s. The hash codes generated by this model were highly aggregated, with very clear edges. In the indicator rating, the highest storage occupancy rating was close to 45 points, the highest detection satisfaction rating was close to 50 points, and the highest detection time rating was close to 30 points. Based on the above data, the proposed improved Deep-Re-Hash detection model shows great potential in processing large-scale image data. It successfully improves the intelligent detection and search efficiency of communication image signals, providing useful reference and inspiration for researchers in related fields.

Hui Liu and Xupeng Liu. “Intelligent Detection and Search Model for Communication Signals Based on Deep-Re-Hash Retrieval Technology”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.9 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150984

@article{Liu2024,
title = {Intelligent Detection and Search Model for Communication Signals Based on Deep-Re-Hash Retrieval Technology},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150984},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150984},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {9},
author = {Hui Liu and Xupeng Liu}
}