With the advancement of technology, the systems of various parts of the picking robots have been increasingly improved. The design of the visual positioning system largely affects the work efficiency of a picking robot, especially its target detection speed, fruit picking accuracy, and target picking environment adaptation. In this study, we propose to use a binocular stereo vision system to acquire images of camellia oleifera fruit targets and then collect and calculate depth information to build our own VOC dataset of Camellia oleifera fruits. The you only look once v3 (YOLOv3) target detection algorithm is adopted to achieve Camellia oleifera fruit recognition in complex environments. The function of locating Camellia oleifera fruit targets is intuitively demonstrated by a newly designed upper computer interface. Experimental results show that compared with other methods, the proposed method has a higher recognition rate and a faster recognition speed, which demonstrates the superiority of its algorithm in complex environments.