In the global monitoring of smart cities, the demands of global object detection systems based on cloud and fog computing in intelligent systems can be satisfied by photographs with globally recognized properties. Nevertheless, conventional techniques are constrained by the imaging depth of field and can produce artifacts or indistinct borders, which can be disastrous for accurately detecting the object. In light of this, this paper proposes an artificial intelligence-based gradient learning network that gathers and enhances domain information at different sizes in order to produce globally focused fusion results. Gradient features, which provide a lot of boundary information, can eliminate the problem of border artifacts and blur in multi-focus fusion. The multiple-receptive module (MRM) facilitates effective information sharing and enables the capture of object properties at different scales. In addition, with the assistance of the global enhancement module (GEM), the network can effectively combine the scale features and gradient data from various receptive fields and reinforce the features to provide precise decision maps. Numerous experiments have demonstrated that our approach outperforms the seven most sophisticated algorithms currently in use.

In the environmental security monitoring of smart cities, the infrared and visible image fusion method deployed on intelligent systems based on cloud and fog computing plays an vital role in providing enhanced images for target detection systems. However, the fusion quality can be significantly influenced by the illumination of the monitoring scenario in visible images. Therefore, conventional methods typically suffer a severe performance drop under the condition of insufficient illumination. To tackle this issue, we propose an illumination enhancement and target-aware fusion method (IETAFusion) based on artificial intelligence, which breaks the boundaries between the task of illumination enhancement and image fusion and provide a fusion result with better visual perception in nighttime scene. Specifically, we use a light-weight contrast enhancement module (CEM) restore the brightness of the visble image. Moreover, a Swin Transformer-based backbone network (STBNet) is utilized to facilitate information exchangement between the source images and enhance the capabilities of target awareness. Finally, the fused images are reconstructed by the contrast-texture retention module (CTRM) and reconstructor. The extensive experiments indicates that the proposed approach achieves improved performance both in human perception and quantitative analysis compared with the state-of-the-art (SOTA) methods.