This research tackles the challenge of precisely segmenting military and army objects within intricate visual environments that are influenced by factors such as camouflage, clutter, occlusions, and variations in lighting. A hybrid framework is introduced that merges Graph Cut-based energy minimization with traditional image processing techniques and pre-detection using convolutional neural networks. This method combines appearance characteristics and spatial constraints to enhance robustness and the accuracy of boundaries. Results from experiments on significant military datasets indicate improved segmentation consistency and clarity, rendering the approach suitable for image analysis tasks that are critical for safety and defense.