# Study of Surface Defect Detection Techniques in Grinding of SiCp/Al Composites
## 1 Introduction  
Silicon-carbide-particle-reinforced aluminum matrix (SiCp/Al) composites have excellent physical and mechanical properties [1]. They exhibit higher strength-to-weight ratios, hardness, stiffness, wear resistance, etc., compared to conventional metals and alloys [2]. As a result, SiCp/Al composites are widely used in the aerospace, automotive, and electronics fields, amongst others [3]. In practice, the processing quality of materials is highly demanded in various areas of precision and ultra-precision production and manufacturing. However, the addition of silicon carbide particles, while giving SiCp/Al composites excellent properties, also weakens the machinability of SiCp/Al composites [4], resulting in poor machined surface quality and the presence of various defects [5, 6]. Therefore, a reduction in machined surface defects and improvements in machined surface quality are important for the further development and application of SiCp/Al composites. Since machined surface defects directly affect machined surface quality, the accurate detection and characterization of machined surface defects is a prerequisite for the effective control of machined surface quality. With the development of computer image processing techniques, methods have emerged to obtain images using machine vision devices and to judge surface defect information in images through image processing. In the early days, defects were evaluated using only image processing and feature extraction methods,which were poorly adapted, inefficient, and did not provide intelligent evaluation results. Therefore, a surface defect detection method combining machine vision and machine learning is proposed to replace the role played by the human brain, using machine learning to judge the presence of defects and thus transforming surface defect detection based on machine vision into automation and intelligence [7]. With improvements in the performance of deep learning, deep learning-based surface defect detection has become the dominant method for surface defect detection due to its high detection accuracy and fast detection speed [8]. Currently, deep learning-based material surface defect detection algorithms can be categorized into two-stage defect detection algorithms and single-stage defect detection algorithms according to their different structures. The two-stage defect detection algorithm first extracts the image features, obtains candidate regions based on the feature maps, and then performs a sliding-window operation on the candidate regions to obtain location and category information on the defects. Representative two-stage defect detection algorithms include R-CNN [9], SPP-Net [10], Fast R-CNN [11], Faster RCNN [12], and R-FCN [13]. The single-stage defect detection algorithm generates candidate boxes while performing classification and regression tasks, which are represented by the YOLO [14] and SSD [15] series networks. In contrast to the two-stage defect detection algorithm, the YOLO series of algorithms uses a neural network model to implement an end-to-end training process, which is an overall framework structure with a smaller number of parameters and faster running speed. The YOLOv4 [16] algorithm can not only achieve a high level of detection accuracy compared to other versions in the YOLO series but also is faster and better able to meet the real-time detection requirements of the industrial sector. As a result, a large number of scholars have applied YOLOv4-based defect detection algorithms for detecting defects on PCBs [17], chips [18], railways [19], fabrics [20,21], transmission line insulators [22,23], etc. The results show that the proposed defect detection algorithm performs well in terms of accuracy and detection speed. However, few deep learning-based methods have been reported for surface defect detection in SiCp/Al composites.