The detection of defects in industrial products is a crucial task in quality control and manufacturing processes. Among various components produced in industries, rubber rings are widely used in many applications, including automotive, aerospace, and machinery, where they function as seals and gaskets. Given their importance, ensuring that these rubber rings are defect-free is essential for maintaining product reliability and safety. However, manually inspecting rubber rings for defects is a labor-intensive and error-prone process. With the rapid advancement of machine learning, particularly neural networks, there is significant potential to automate this task. A key challenge, however, lies in designing neural network models that are not only accurate but also efficient in terms of computational resources and processing time. This is particularly important in industrial settings, where real-time performance and resource constraints are often critical factors. The optimization of lightweight neural networks for rubber ring defect detection addresses the need for a model that can perform high-accuracy defect detection while minimizing the computational burden.
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