Bounding box localization and regression are crucial in modern computer vision applications. Traditional bounding box methods often require resizing the image, which affects the preservation of details. We introduce Energy Box, a novel framework that optimizes bounding boxes at the original image resolution through an energy-based approach. Our method incorporates three energy components: intrinsic (regional consistency), extrinsic (boundary properties), and contextual (spatial-semantic relationships), which are well-balanced to handle objects at different scales without losing information. Experiments on PASCAL VOC2012 with three specialized optimization scenarios achieve an IoU of 0.994–0.999, with a processing speed of 3.64–5.14 s/object, demonstrating its superior performance for surveillance applications and automated systems.