Circle detection in images is one of the key technologies in machine vision, pattern recognition, and artificial intelligence. However, conventional circle detection methods are sensitive to complex scenes, noise, and occlusion in images. Solving the impact of these situations is still the focus of circle detection algorithm research. Therefore, a circle detection algorithm based on neighborhood density clustering (NDC) is proposed. The proposed algorithm calculates circle parameters of connected regions after extracting corners, corroding the corners, and marking the connected regions. Then, NDC of the circle parameters is executed to classify arcs belonging to the same circle into one category to acquire a virtual connected region. And the circle parameters of the virtual connected region are clustered using NDC again to obtain the circle parameter dataset of the circle to be detected. The precise circle parameters are further estimated by calculating the centroid of each category. To prevent false positives, candidate circles are verified through a ratio rule. Extensive experiments using both synthetic and real images were performed. The results compared with those of representative state-of-the-art methods demonstrate that the proposed algorithm can be applied to a variety of complex scenes and has several advantages: good anti-occlusion effect, more robustness against noise, high accuracy, and better performance.