# Lightweight Neural Network Optimization for Rubber Ring Defect Detection
## 1 Introduction  
Rubber O-ring seals are one of the most commonly used sealing structures in mechanical systems [1]. However, during the production of rubber rings, because of the instability of the manufacturing process, some undesirable problems can occur after plastic molding. Common problems include cracks (Figure 0(a)), chips (Figure 0(b)), impurities (Figure 0(c)), and wear (Figure 0(d)) [2]. These defects can directly lead to poor sealing and cause leakage, which, in turn, can lead to major accidents [3]. In the current industrial manufacturing landscape, the identification of flaws in O-rings mainly depends on manual inspection. This approach not only amplifies production expenses and augments the workload for laborers but also leads to reduced efficiency in detection and substantial inaccuracies, thereby compromising the reliability of the results. Machine vision inspection technology, characterized by its non-contact, online, objective, and automated attributes, presents significant advantages and is well-suited for product inspection in modern manufacturing industries [4]. However, the machine vision-based approach is reliant on manually designed defect features, rendering it principally appropriate for detecting surface defects in products with uniform shapes and singular coloration. Nonetheless, the manual extraction of surface defect features in practical production remains a challenging task, often impeding accurate detection. Although numerous sets of defect templates can be designed using manual defect feature extraction, this method can still only describe a finite number of faults and is unable to identify new types of defects. When traditional methods encounter complex and irregular data, they are not only difficult to apply but may also require complex post-processing procedures. Therefore,the sole application of machine vision technology is not entirely suitable for the industrial inspection of rubber rings. In recent years, deep learning algorithms based on convolutional neural networks (CNNs) have gained considerable applications across various industries, particularly in the field of detection [5; 6; 7; 8; 9; 10; 11; 12; 13]. These methods are used for industrial defect detection when the defect types are known, sufficient labeled samples are available, or when the problem of the multi-classification of defect types needs to be solved. In an application example of bridge defect detection using CNNs, [PERSON] et al. [14] presented a two-stage detector based on the Faster R-CNN to improve the detection accuracy and changed the backbone network to ZF-net to improve the check speed. [PERSON] et al. [15] improved the detection accuracy of steel plate defects using a multilevel feature fusion network based on a CNN. [PERSON] et al. [16] detected steel surface defects based on a single-stage detector (\"You Only Look Once\" [YOLO]) and fused shallow features to improve the detection of fine defects. [PERSON] et al. [17] used YOLOv3 [18] to detect bridge defects and used migration learning, batch renormalization, and focal loss to improve detection performance. [PERSON] et al. [19] combined DenseNet [20] with YOLOv3 to detect LED defects. According to the cited literature, the core developmental philosophy of CNNs in defect detection is centered around the use of multi-scale fusion and data enhancement to improve detection accuracy. Rubber rings are in great demand and produced in large quantities in various industries. Fast and precise rubber ring defect detection enables the timely rejection of defective products. Numerous researchers have focused on lightweight neural networks as a solution to the issue of how to increase the detection speed while maintaining the highest level of detection accuracy. [PERSON] et al. [21] proposed SqueezeNet, used a 1 \(\times\) 1 filter to replace the 3 \(\times\) 3 filter, reduced the number of input channels of the 3 \(\times\) 3 filter, and used the Fire module, which made the network size 50 times smaller than that of AlexNet but maintained the same accuracy.