# FIQ: A Fastener Inspection and Quantization Method Based on Mask FRCN
### Related Works in Existing Literatures  
In the early stages of computer vision development, researchers employed traditional image processing techniques for fastener detection. [PERSON] [1] utilized the gray and gradient characteristics of the sleeper region to locate fasteners, followed by an Adaboost-based cascade classifier for detection. [PERSON] [2] extracted Haar features for classification, creating a high-speed defect detection model. [PERSON] [3] introduced a direction field-based method for high-speed railways, and [PERSON], [PERSON] [4] developed an automatic detection system using MLPNCs. However, these methods are limited by their requirement for balanced positive and negative samples, which is a significant constraint in real-world scenarios where imbalanced datasets are common. The reliance on balanced datasets by traditional methods is a notable limitation, as it does not reflect the reality of railway maintenance where defective fasteners are relatively rare. This highlights the need for algorithms that can effectively learn from imbalanced data.  
In recent years, deep learning has been applied to fastener detection. [PERSON] et al. [5] used Faster-RCNN and YOLO V3 for railway area detection and fastener state identification. [PERSON] [6] proposed a multi-size input training method, and [PERSON] et al. [7] introduced a hierarchical features-based model. [PERSON] et al. [8] and [PERSON] et al. [9] proposed deep learning approaches that improved performance. However, these methods still struggle with the imbalance issue and often require extensive data augmentation. Despite the advancements in deep learning, the inability of these methods to fully address the sample imbalance problem indicates a need for more innovative solutions. These approaches, although promising, still rely heavily on data augmentation, which may not always capture the nuances of real-world fastener defects.  
Several studies have focused on the issue of imbalanced fastener samples. [PERSON] et al. [10] used U-Net to obtain images containing only fasteners, then employed generative adversarial networks (GANs) to generate more fastener samples, and finally spliced the generated fastener images with blank track pictures to create new samples. [PERSON] et al. [11] introduced the concept of GANs, which have been widely used for data augmentation in various applications. [PERSON] et al. [12] employed dense fully convolutional segmentation for computerized tomography scans for contactless monitoring of railway track geometry. [PERSON] et al. [13] proposed a railway fastener detection method based on the optimal selection of features and classifiers. Although these approaches address the positive and negative sample imbalance from one perspective, their essence still lies in dataset expansion to achieve a relatively balanced state of positive and negative samples. It does not genuinely eliminate the problem of sample imbalance. Furthermore, although these methods offer novel ideas for data expansion, they only demonstrate image segmentation results for fasteners under unbalanced data. The determination of fastener states obtained from image segmentation still requires human observation and a computer cannot realize one-stop detection from image input to result judgment.