The intrusion of metal foreign objects will lower the efficiency and stability of wireless power transfer systems, even causing safety issues, thus it is extremely essential to achieve metal object detection. Aiming at the problem that existing technologies are subjected to blind-zone and cannot detect small foreign objects, a metal object detection method that combines deep learning-based object segmentation and support vector machine (SVM)-based object classification is proposed. First, object segmentation is performed using a YOLO v3 neural network based on RGB image of the charging area. Then the corresponding hyperspectral images of each object region are classified by the SVM. Finally, an experimental platform is built to verify the effectiveness of the proposed method. Results show that the proposed method not only detects tiny metal object such as a screw nut and a paper clip, but also has the potential to detect metal objects wrapped by non-metal material. Compared with the pixel-by-pixel detection using SVM alone, the proposed method improves the detection speed by about 38. 9%.