Data samples were always limited for multi-category object detection in some specific scenes. In order to improve the stability and accuracy of the light-weight neural networks for small object recognition in robotic system, an object status recognition module based on Robotic Operating System (ROS) was designed. Firstly, considering the computing power limitation of embedded devices, a lightweight network YOLO-tiny was used as the main architecture of object recognition model, then the Respective Field Block (RFB) was introduced in YOLO-tiny, so as to construct the YOLO-tiny-RFB model. Secondly, MobileNet was employed to conduct an accurate classification of multiple statuses of rotary switches. Finally, the data association rules were designed, and algorithms such as image alignment and Intersection Over Union (IOU) calculation were used to make the recognition module complete the fusion of multiple recognition results of the same scene, so that users were able to track the statuses of each meter at different times. Experimental results show that on the constructed power station instrument recognition dataset, compared with the YOLO-tiny, the YOLO-tiny-RFB model increases the object recognition mean Average Precision (mAP) by 17.9%, which is achieved to 82.4% with a small increase in computational load of model. In the case of extremely unbalanced rotary switch data distribution, the average accuracy of model reaches 90.7% by introducing various data enhancement methods. The proposed object detection module and status recognition network model can complete the status recognition of all kinds of instruments effectively and accurately, meanwhile they can fuse the recognition results of instrument status at different times.