Image fusion algorithm for traffic accident rescue based on deep learning

Aiming at the problem that fire,smoke and other situations at the scene of serious traffic accidents affect the detection equipment to complete the search and rescue of trapped persons, a Convolutional Block Attention Module-Improved Loss-Dual-Discriminator Conditional Generative Based on Convolutional Attention Mechanism Adversarial Network(CBAM-IL-DDCGAN)infrared and visible image fusion method is proposed. Firstly,the decoding network with attention feature fusion module is used to restore and reconstruct the image from space and channel. Secondly,an adaptive weight calculation method based on gradient information is designed. Finally,the test experiment of fused image continuous frames was carried out. Experimental results show that the proposed image fusion algorithm performs well, and achieves a significant improvement of more than 7% in PSNR,SSIM and MSE compared with the traditional algorithm and the generative adversarial network algorithm. These results verify the feasibility and superiority of the fusion algorithm in complex traffic accident rescue. © 2023 Editorial Board of Jilin University. All rights reserved.