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The detection of the most salient parts of images as objects in salient object detection tasks mimics human behavior, which is useful for a variety of computer vision applications. In this paper, the Local and Global Saliency Driven Dual-Channel Pulse Coupled Neural Network (LGSD-DCPCNN) model is used to provide a novel strategy for saliency detection. To achieve visually homogeneous sections and save computation costs, the input image is first subjected to superpixel segmentation. The global and local saliency maps are then created using the segmented image’s position, color, and textural properties. The LGSD-DCPCNN network is activated using these saliency maps to extract visually consistent features from the input maps to provide the final saliency map. An extensive qualitative and quantitative performance study is undertaken to assess the efficacy of the proposed method. When compared to state-of-the-art approaches, the experimental results show a considerable improvement in the detection of salient regions. Quantitative analysis of the proposed method reveals a significant improvement in the area under the ROC curve (AUC) score, F-measure score, and mean absolute error (MAE) score. The qualitative analysis describes the proposed algorithm’s ability to detect multiple salient objects accurately while maintaining significant border preservation.