Object discovery refers to recognising all unknown objects in images, which is crucial for robotic systems to explore the unseen environment. Recently, object detection models based on deep learning have shown remarkable achievements in object classification and localisation. However, these models have difficulties handling the unseen environment because it is infeasible to exhaustively predefine all types of objects. In this paper, we propose the model YOLOv4-object to recognise all objects in images by modifying the output space of YOLOv4 and related image labels. Experiments on COCO dataset demonstrate the effectiveness of our method by achieving 67.97% recall (6.49% higher than vanilla YOLOv4). We point out that the incomplete labels (COCO only labels for 80 categories) hurt the learning process of object discovery and a higher recall can be achieved by our method if the dataset is fully labelled. Moreover, our approach is transferable, extensible, and compressible, showing broad application scenarios. Finally, we conduct extensive experiments to illustrate the factors that affect the object discovery performance of our model and some suggestions on practical implementations are elaborated.