In large industrial plants, due to a wide variety and a large number of equipment control switches, the complexity of operating procedures and the subjectivity of human judgment may lead to operational errors and cause serious consequences in the daily operation and maintenance process. To assist operators in accurately judging whether the state of an equipment switch is correct, an improved Faster R-CNN algorithm is proposed for state recognition of equipment switches. Firstly, the dilated residual network (ResNet) is used as the feature extraction network, and the multi-branch dilated convolution is introduced into ResNet50 to fuse the information of different receptive fields. Secondly, the feature pyramid network is improved by the addition of a bottom-up feature enhancement branch to the original network, which is used to integrate multi-scale feature information. Then, the K-means++ algorithm is applied to cluster bounding boxes of switches, and the size of proposals for equipment switches is designed. Finally, the non-maximum suppression (NMS) algorithm is replaced with Soft-NMS to reduce the influence of switch overlap on the detection effect and enhance the performance of suppressing the overlapping proposals. On a switch state dataset, the mean average precision (mAP) of the improved Faster R-CNN reaches 91.5%. Moreover, it has been applied to assist state recognition of equipment switches in the daily operation and maintenance of pumped-storage power stations to meet the needs of intelligent supervision in complex scenarios.