MCAW-YOLO: An Efficient Detection Model for Ceramic Tile Surface Defects
Source abstract
Abstract
Traditional manual visual detection methods are inefficient, subjective, and costly, making them prone to false and missed detections. Deep-learning-based defect detection identifies the types of defects and pinpoints their locations. By employing this approach, we could enhance the production workflow, boost production efficiency, minimize company expenses, and lessen the workload on workers. In this paper, we propose a lightweight tile-defect detection algorithm that strikes a balance between model parameters and accuracy. Firstly, we introduced the mobile-friendly vision transformer into the backbone network to capture global and local information. This allowed the model to comprehend the image content better and enhance defect feature extraction. Secondly, we designed a lightweight feature fusion network. This design amplified the network’s detection capability for defects of different scales and mitigated the blurriness and redundancy in the feature maps while reducing the model’s parameter count. We then devised a convolution module incorporating the normalization-based attention module, to direct the model’s focus toward defect features. This reduced background noise and filtered out features irrelevant to defects. Finally, we utilized a bounding box regression loss with a dynamic focusing mechanism. This approach facilitated the prediction of more precise object bounding boxes, thereby improving the model’s convergence rate and detection precision. Experimental results demonstrated that the improved algorithm achieved a mean average precision of 71.9%, marking a 3.1% improvement over the original algorithm. Furthermore, there was a reduction of 26.2% in the model’s parameters and a 20.9% decrease in the number of calculations.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Advanced Neural Network Applications · 3D Surveying and Cultural Heritage
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
The College of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China
The Key Laboratory of Images and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China
Funding
Ningxia Key Research and Development Program (Key Project) · Ningxia Key Research and Development Program (Talent Introduction Special Project) · North Minzu University 2022 School-level Research Platform, “Digital Agriculture Empowerment for Ningxia Rural Revitalization Innovation Team” · Yinchuan City School-Enterprise Joint Innovation Project · National Ethnic Affairs Commission
References (38)
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