Efficient Neural Network Optimization for Rubber Ring Defect Detection

Ashraf Ying^*
*Correspondence: Ashraf Ying, Department of Artificial Intelligence, Chongqing University of Technology, Yubei, China, Chongqing University of Technology, China, Email:

Introducción

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. Traditional deep learning models, such as convolutional neural networks (CNNs), have proven effective for image-based defect detection. However, these models typically require large amounts of data, substantial computational resources, and significant memory. This makes them less suitable for deployment in resource-constrained environments such as embedded systems or industrial machines that may not have access to high-performance hardware. Therefore, optimizing neural networks to reduce their size and complexity without sacrificing accuracy is a critical research direction in the field of automated defect detection.

Description

To effectively optimize neural networks for rubber ring defect detection, several strategies can be employed. The first step in the process involves choosing the appropriate architecture for the neural network. Convolutional neural networks are well-suited for image-based tasks, as they are capable of capturing spatial hierarchies and local features in images. However, these networks can become computationally expensive as they increase in depth and complexity. One approach to address this issue is to use lightweight CNN architectures designed to achieve a balance between performance and computational efficiency. Examples of such architectures include MobileNet, SqueezeNet, and ShuffleNet, all of which are designed with the goal of reducing the number of parameters and computations required for inference while maintaining competitive accuracy. MobileNet, for instance, utilizes depthwise separable convolutions instead of the standard convolutions found in traditional CNNs. This reduces the number of parameters by a factor of about 8, which directly reduces the computational cost. SqueezeNet, on the other hand, uses fire modules, which consist of a squeeze layer that reduces the dimensionality of the input and an expand layer that increases the dimensionality. This helps to reduce the model size while preserving the modelâ??s ability to capture important features from the input image. Similarly, ShuffleNet leverages group convolutions and channel shuffling to reduce the computational cost of convolutional operations. These architectures provide an excellent starting point for optimizing neural networks for rubber ring defect detection, as they are specifically designed to work efficiently in mobile and embedded environments [1]. In addition to selecting a lightweight architecture, model pruning is another effective optimization technique. Pruning involves removing unnecessary weights or neurons from the neural network, effectively reducing the model's size and complexity. This process can be performed in several ways, such as by removing neurons with small weights or using more advanced methods like dynamic pruning, where the pruning decision is made during training. Pruning can significantly reduce the number of operations required during inference, making the network more efficient without significantly compromising its performance. When applied to the detection of rubber ring defects, pruning can help improve the modelâ??s ability to run on embedded systems that require fast and efficient processing. Another important optimization technique for lightweight neural networks is quantization. Quantization reduces the precision of the weights and activations of the network, allowing the model to use fewer bits to represent these values. This reduces the memory requirements and speeds up the inference process. For example, instead of using 32-bit floating-point numbers to represent weights, a network can use 8-bit integers. Quantization can be applied to both the weights and activations of the network, leading to further reductions in model size and computational load. This technique is particularly useful for deploying neural networks on devices with limited memory and processing power, such as microcontrollers or mobile phones. In the case of rubber ring defect detection, the optimized and quantized model can run on embedded devices that are integrated into production lines, enabling real-time defect detection with minimal latency [2]. Another critical aspect of neural network optimization is the efficient use of data. While large datasets can improve the accuracy of defect detection models, obtaining a large labeled dataset for rubber ring defects can be challenging and time-consuming. Data augmentation techniques can be employed to artificially expand the training dataset, thus improving the modelâ??s robustness and generalization capabilities. Common data augmentation techniques for image-based tasks include random rotations, flips, translations, and color adjustments. These techniques can simulate different perspectives, lighting conditions, and wear-and-tear patterns, which are common in real-world rubber ring defects. By augmenting the data in this way, the neural network can become more resilient to variations in the input images and improve its ability to detect defects under different conditions. Transfer learning is another technique that can be utilized to optimize neural networks for rubber ring defect detection. Transfer learning involves using a pre-trained model, often trained on a large dataset, as a starting point for training the target model on a smaller, domain-specific dataset. This approach helps to reduce the amount of labeled data required for training and speeds up the training process. In the context of rubber ring defect detection, a pre-trained CNN model, such as one trained on a large image classification dataset like ImageNet, can be finetuned on a smaller dataset of rubber ring images. By leveraging the knowledge learned from the large dataset, the model can generalize better to the specific task of defect detection, even with a limited amount of labeled data [3]. The performance of the optimized lightweight neural network for rubber ring defect detection can be evaluated using various metrics, including accuracy, precision, recall, and F1-score. Accuracy measures the overall percentage of correct predictions, while precision and recall focus on the performance with respect to the positive class (i.e., defect detection). Precision represents the proportion of true positive predictions out of all positive predictions, and recall measures the proportion of true positive predictions out of all actual positive instances. The F1-score is the harmonic mean of precision and recall, providing a balanced measure of the modelâ??s performance. These metrics are essential in evaluating the effectiveness of the model, as detecting defects in rubber rings requires a high level of precision and recall to avoid both false positives (misidentifying a defect when there is none) and false negatives (failing to identify a defect when one is present). Real-time defect detection is crucial in industrial applications, as defects in rubber rings can lead to product failures, safety issues, and costly downtime in production lines. Therefore, the optimized neural network should be capable of making predictions in realtime, processing images quickly enough to allow for immediate feedback and corrective actions. The lightweight architecture, model pruning, quantization, and efficient data augmentation techniques all contribute to reducing the inference time and making real-time defect detection feasible. By optimizing the model for efficiency, manufacturers can integrate defect detection systems into their production lines without requiring expensive hardware or causing delays in the production process [4,5].

Conclusion

Optimization of lightweight neural networks for rubber ring defect detection provides a powerful solution to the challenges faced in automated quality control. By employing techniques such as architecture selection, pruning, quantization, data augmentation, and transfer learning, it is possible to create an efficient model that can accurately detect defects in rubber rings while minimizing the computational resources required. These optimized models can be deployed in real-time on embedded systems, making them suitable for industrial environments where speed and efficiency are critical. As the manufacturing industry continues to embrace automation and smart technologies, the role of optimized neural networks in defect detection will only grow, driving improvements in product quality and operational efficiency.

References

1. Liang, Banglong, Xi Yang, Zili Wang and Xing Su, et al. "Influence of randomness in rubber materials parameters on the reliability of rubber O-ring seal." Mater 12 (2019): 1566.

Google Scholar, Crossref, Indexed at

2. Lu, Youcun, Lin Duanmu, Zhiqiang John Zhai and Zongshan Wang. "Application and improvement of Canny edge-detection algorithm for exterior wall hollowing detection using infrared thermal images." Energy Build 274 (2022): 112421.

Google Scholar, Crossref, Indexed at