Mechanical Assembly Monitoring Method Based on Semi-Supervised Semantic Segmentation

Abstract

Semantic segmentation of assembly images is to recognize the assembled parts and find wrong assembly operations. However, the training of supervised semantic segmentation requires a large amount of labeled data, which is time-consuming and laborious. Moreover, the sizes of mechanical assemblies are not uniform, leading to low segmentation accuracy of small-target objects. This study proposes an adversarial learning network for semi-supervised semantic segmentation of mechanical assembly images (AdvSemiSeg-MA). A fusion method of ASFF multiscale output is proposed, which combines the outputs of different dimensions of ASFF into one output. This fusion method can make full use of the high-level semantic features and low-level fine-grained features, which helps to improve the segmentation accuracy of the model for small targets. Meanwhile, the multibranch structure RFASPP module is proposed, which enlarges the receptive field and ensures the target object is close to the center of the receptive field. The CoordConv module is introduced to allow the convolution to perceive spatial position information, thus enabling the semantic segmentation network to be position-sensitive. In the discriminator network, spectral normalization is introduced. The proposed method obtains state-of-art results on the synthesized assembly depth image dataset and performs well on actual assembly RGB image datasets.