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Detection-free framework for cabinet switch state recognition

In this paper, we propose a novel detection-free framework for cabinet switch state recognition. Different from prior works which adopt object detection or detection followed by per-switch recognition, our approach processes the image as a whole. Specifically, a semantic segmentation model is used to generate a coarse semantic map as a temporary result, which will be further refined by an object counting module. Moreover, for higher efficiency, we augment the Fully Convolutional Network (FCN) by introducing a hierarchical feature aggregation structure, forming a lightweight yet effective model called HFA-FCN. The experimental results show that the proposed pipeline outperforms those based on object detection, especially in hard cases: images are low-quality, targets are densely distributed or distorted. To the best of our knowledge, we are the first to formulate the task of switch state recognition as a task of semantic segmentation and object counting.