The conventional approach for detecting visual anomalies in artificial systems has drawbacks, such as high cost, low efficiency, and errors in detection. As an alternative, the electrical properties of components can be leveraged for detecting defects in printed circuit boards (PCBs) through a semi-automatic, manual detection method that includes online and functional testing [13]. Researchers have explored various techniques to enhance this method, such as compressing images using wavelet transform to reduce memory and computation requirements [14], using traditional machine learning algorithms for defect detection [15], and designing low-complexity neural network and machine vision schemes to improve defect detection [16]. Other approaches include using Fourier image reconstruction to identify small defects [17] and ultrasonic laser thermal imaging for real-time defect detection [18]. Although these methods can reduce costs compared to manual detection, their limited application is attributed to factors, such as the non-reusability of the test process, the high cost of equipment, and complex writing functions, among others.

Machine vision detection methods have emerged as a viable solution to overcome the shortcomings of traditional artificial detection methods and are increasingly being applied in modern industries [19]. There are three primary categories of PCB defect detection methods based on machine vision: reference, non-reference, and hybrid methods. The reference method [20] typically involves image segmentation techniques to detect defects. For example, Li et al. compared PCB images with and without defects to identify defects [21]. Non-reference methods [22] mainly rely on machine learning algorithms for defect detection. For instance, Malge et al. employed an image segmentation algorithm to detect PCB defects [23]. The hybrid method [24] combines reference and non-reference methods to achieve more accurate defect detection. For example, Ray et al. developed a hybrid detection method by comparing PCB images and using image segmentation techniques [25]. Image segmentation techniques include threshold segmentation, edge segmentation, and region segmentation methods. For example, Ardhy et al. [26] used the adaptive Gaussian threshold segmentation to achieve rapid detection with minimal parameters, but the detection efficacy varied significantly in different areas with light strips. Baygin et al. [27] used Hough transform for edge segmentation and combined it with the Canny operator to enhance detection efficiency. Ma et al. [28] improved the region growth algorithm for region segmentation to achieve better detection outcomes. However, these methods require manual tuning of model parameters, which may lead to suboptimal accuracy and efficiency.

Recent studies have demonstrated that the accuracy of automated optical inspection (AOI) is higher compared to other methods. However, due to the system’s high sensitivity, it has very strict parameter-setting rules and may miss some cases, necessitating manual screening after machine screening is complete [29]. Meanwhile, deep learning technology has been rapidly advancing. Target defect detection methods based on deep learning have shown to be highly accurate, fast, and do not require manual screening. Thus, they are more cost-effective and efficient. Moreover, the parameter-setting rules are not as strict as those in the AOI system. As a result, deep learning-based methods are being increasingly studied and applied in various industries.

Due to advancements in computing technology, complex operations have become more affordable, resulting in the rapid development of neural networks, including a large number of deep neural networks. In the field of PCB defect detection, many scholars have applied deep learning techniques. DenseNet [30] achieved better performance with fewer parameters and computing costs by densely connecting all front and back layers to enable feature reuse. Huang et al. [31] improved detection accuracy and efficiency by designing a convolutional neural network that connects each layer in a feedforward manner. Compared to conventional machine vision methods, deep learning algorithms have stronger nonlinear abilities, higher robustness, and are applicable to more complex scenarios. He [32] proposed an improvement measure that helped achieve a 96.91% accuracy rate. Geng et al. [33] improved the detection accuracy to 96.65% by using focal loss and ResNet50 as the backbone network. Ding et al. [34] designed TDD-net, a detection network specifically aimed at tiny PCB defects, which adopted a multi-scale fusion strategy and applied online hard example mining to enhance the certainty of ROI proposals, resulting in a detection accuracy of 98.90%. Sun et al. [35] proposed the Inception-ResNet-v2 model, which improved the PCB detection accuracy by adding an SE module to part of the structure. Hu et al. [36] presented UF-Net, which retained more defect target information by using the Skip Connect method and achieved a detection accuracy of 98.6%. Li et al. [37] improved the mAP value to 98.71% by replacing the convolution layer in the trunk with the residual structure unit CSP based on the YOLOv4 algorithm. Wang et al. [38] proposed a lightweight model that used the ShuffleNetV2 structure in the YOLOv5 backbone and achieved an accuracy of 95%. YOLOv7, as a classic representative of the target detection algorithm, has surpassed the previous YOLO series in detection speed and accuracy.