Shadow detection is useful in a variety of image analysis applications, as it can improve scene understanding. Most of the recent shadow detection approaches use near-infrared (NIR) cameras and deep learning to provide enhanced segmentation of the shadow areas in images. In this paper a novel shadow detection method is proposed, exploiting the perceptual color representation of the HSV color space and a physics-inspired optimization algorithm for image segmentation. The comparative advantage of this method over the state-of-the-art ones is that its performance is comparable without requiring any special equipment, such as NIR cameras, while it is simpler. Quantitative and qualitative experiments on publicly available datasets in comparison with three state-of-the-art methods, validate its effectiveness.