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Flexible Flat Cable (FFC) detection is the premise of robot 3C assembly and is challenging because FFCs are often non-axis aligned with arbitrary orientations having cluttered surroundings. However, to date, the traditional robotic object detection methods mainly regress the object horizontal bounding box (HBB), in which the size and aspect ratios do not reflect the actual shape of the target object and hardly separate the FFCs in dense. In this paper, rotated object detection was introduced into FFC detection, and a YOLO-based arbitrary-oriented FFC detection method named YOLOOD was proposed. Firstly, oriented bounding boxes (OBB) are used to reflect the object's physical size and angle information and better separate the FFCs from the dense background. Secondly, the circular smooth label (CSL) angular classification algorithm is adopted to obtain the angle information of FFCs. Finally, the head point regression branch is introduced to distinguish between the head and the tail of the FFC and expand the range of FFC detection angle to [0 •, 360 • ). The proposed YOLOOD can reach the detection performance with an average precision of 90.82% and a detection speed of 112 FPS on an FFC dataset. Meanwhile, an actual FFC grasping experiment demonstrated the proposed YOLOOD's effectiveness and feasibility

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