Semantic Segmentation of Assembly Images Combining Deep Learning and Ontological Reasoning
Year: 2025
Type: article
Abstract: With the advancement of industrial intelligence, semantic segmentation of assembly images is increasingly widely used in automated production and other fields. Aiming at the problem that traditional image segmentation methods often neglect the semantic association and structural information between objects in the image, this paper proposes a semantic segmentation method for assembly body images that combines deep learning and ontology reasoning. DeepLabV3+ is used as the base model, and MobileNetV2 backbone network and Convolutional Block Attention Module (CBAM) are introduced to accelerate the computational speed of the network and at the same time enhance the fusion ability of multi-scale features of the network to further improve the semantic segmentation accuracy. However, due to the poor interpretability and reliability of the data-driven deep learning method, an ontology module with inference capability is introduced and semantic rules are written based on the relevant knowledge of the domain to correct the misclassification of deep learning. Finally, we make the reducer model through Solidworks as the dataset for the experiment. The experimental results show that the improved network model improves 11.92% and 6.53% over the original network model in mIoU and mPA, respectively. The constructed ontology model of assembly information is then referenced and the misclassified parts are corrected by reasoning. The effectiveness of the semantic segmentation method proposed in this paper is verified.
Source: IEEE Access
Authors: Han Zhang, Xiaolin Shi, Haisong Xu, Yi Li, Liping Ma
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
Awards: G7424852594, G7459320067
OpenAlex ID: W4409473859