Salient Object Detection experiences significant difficulties when trying to identify objects from single haze images due to the deterioration of visibility and low contrast. To subdue this challenge, this study introduces a computational model of visual saliency as a solution. Object detection in hazy environments presents a major challenge due to reduced visibility and contrast. The proposed methodology begins by determining whether an image is hazy, and if so, leverages the Dark Channel Prior (DCP) to extract essential haze-related information. The DCP calculation serves as the basis for subsequent dehazing, achieved through the Multiscale Retinex algorithm. In the dehazing phase, the Multiscale Retinex algorithm is applied to improve image clarity and obtain a dehazed version. This haze-free image is given as input to a trained U-Net architecture, which gives a saliency map that identifies notable and prominent regions within the image. Simultaneously, it undergoes region-based segmentation. The geodesic saliency map is calculated using geodesic distance, considering both spatial proximity and feature similarity. In the final step, the saliency maps generated from the U-Net and geodesic saliency computation are fused to generate the ultimate saliency map. The effectiveness of the suggested method in detecting salient objects in hazy images is supported by the experimental findings, which showcase state-of-the-art performance in dehazing. The integration of DCP, multiscale Retinex, and dual saliency maps enhances both dehazing and object detection, making this method valuable in a variety of computer vision applications, including autonomous driving, video surveillance, and image restoration. The experimental results of AUC and MAE provide confirmation for the effectiveness and accuracy of the saliency computational model that has been proposed.