The authors propose a novel object detection algorithm based on shape matching using a single sketch of an object. The proposed algorithm uses circular arc segments to describe image edges; this approach is advantageous for shape description, shape expression and reconstruction. Circular arcs are initially segmented from the image contour using the split-and-merge method, and they are extended, being partially overlapped with neighbouring circular arcs. The extracted circular arcs of the object sketch constitute an attributed relational graph as a structured object model. Circular arcs in the test image are refined by the bottom-up process of circular arc extension, and matched with circular arcs in the object model by the top-down process of end-point adjustment. The authors design end-point-based shape descriptors to encode local shape information. Hough voting aggregates the detection of circular arcs to localise the object. Probabilistic relaxation verifies the detection candidates and delineate the object boundaries. The proposed object detection system benefits from reliable extraction of contour segments, efficient and discriminative shape encoding, and flexible and robust shape matching. It exhibits competitive object detection performance in experiments using real images.