In this paper, we present a new combination of a biologically inspired attention system (VOCUS – Visual Object detection with a CompUtational attention System) with a robust object detection method. As an application, we built a reliable system for ball recognition in the RoboCup context. Firstly, VOCUS ﬁnds regions of interest generating a hypothesis for possible locations of the ball. Secondly, a fast classiﬁer veriﬁes the hypothesis by detecting balls at regions of interest. The combination of both approaches makes the system highly robust and eliminates false detections. Furthermore, the system is quickly adaptable to balls in different scenarios: The complex classiﬁer is universally applicable to balls in every context and the attention system improves the performance by learning scenario-speciﬁc features quickly from only a few training examples.