Image-based road detection is a vital task for many real-world applications, such as autonomous driving and obstacle detection. We propose a method for segmenting road regions from a single image based on horizon line estimation and clustering technology. The key idea is to leverage normalized cross correlation to search for the line separating the road image. Additionally, we divide the lower part of the road image into several identical parts horizontally and utilize a density-peak clustering algorithm in terms of gray and HSV value of each pixel. Clustering results are further labeled as road and nonroad based on the assumption that two adjacent horizontal parts share similar clustering size and average gray value. For road images with shadows, we also propose a new shadow-free space derived from HSV space. By calculating maximum entropy and reconstructing images, we show that shadows are eliminated to achieve better road detection. Experimental results on four datasets and shadow images demonstrate the effectiveness and robustness of our method.