Research on PCB defect detection in intelligent ships based on hybrid CN-YoLov5

In the process of PCB manufacturing, with the increasing demand, the complexity of IC chips, the number of pins, and the density are getting higher and higher. Therefore, the rationality of PCB layout is particularly important. In many links of PCB production and processing, the quality of solder joints needs to be tested. In recent years, in order to improve the automation and efficiency of PCB solder joint quality inspection, the automatic optical inspection system of PCB solder joint based on CCD camera image has received more and more attention. In this paper, a PCB defect detection and enhancement system based on MATLAB is studied aiming at the problem of PCB solder joint location and defect detection. The main tasks of the system include PCB surface feature image acquisition, image enhancement and segmentation preprocessing, PCB surface feature extraction, neural network model establishment and defect identification, etc., completing PCB defect location and establishing PCB defect data set, in which BP neural network is used as the judgment model of this project, It can meet the current PCB surface defect monitoring requirements.

Nowadays, industrial electronic products are integrated into all aspects of life, with PCB quality playing a decisive role in their performance. Ensuring PCB factory quality is thus crucial. Common PCB defects serve as key references for evaluating quality. To address low detection accuracy and the bulky size of existing models, we propose an improved PCB-YOLO model based on YOLOv8n.To reduce model size, we introduce a novel CRSCC module combining SCConv convolution and C2f, enhancing PCB defect detection accuracy and significantly reducing model parameters. For feature fusion, we propose the FFCA attention module, designed to handle PCB surface defect characteristics by fusing multi-scale local features. This improves spatial dependency capture, detail attention, feature resolution, and detection accuracy. Additionally, the WIPIoU loss function is developed to calculate IoU using auxiliary boundaries and address low-quality data, improving small-target recognition and accelerating convergence. Experimental results demonstrate significant improvements in PCB defect detection, with mAP50 increasing by 5.7%, and reductions of 13.3% and 14.8% in model parameters and computational complexity, respectively. Compared to mainstream models, PCB-YOLO achieves the best overall performance. The model’s effectiveness and generalization are further validated on the NEU-DET steel surface defect dataset, achieving excellent results. The PCB-YOLO model offers a practical, efficient solution for PCB and steel defect detection, with broad application prospects.

Recently, many companies have introduced automated defect detection methods for defect-free PCB manufacturing. In particular, deep learning-based image understanding methods are very widely used. In this study, we present an analysis of training deep learning models to perform PCB defect detection stably. To this end, we first summarize the characteristics of industrial images, such as PCB images. Then, the factors that can cause changes (contamination and quality degradation) to the image data in the industrial field are analyzed. Subsequently, we organize defect detection methods that can be applied according to the situation and purpose of PCB defect detection. In addition, we review the characteristics of each method in detail. Our experimental results demonstrated the impact of various degradation factors, such as defect detection methods, data quality, and image contamination. Based on our overview of PCB defect detection and experiment results, we present knowledge and guidelines for correct PCB defect detection.

In the electronic industry product quality control, PCB defect detection is a crucial part, which has the characteristics of small defect size and high similarity. The existing defect detection methods are still not good enough for detecting small target defects; therefore, the algorithm in this paper proposes an improved algorithm for PCB defect detection based on the RetinaNet model. The ResNet-D residual structure and efficient channel focus module are introduced in the model backbone network to enhance its feature extraction capability and achieve the purpose of improving the detection accuracy. At the same time, the method replaces the original multi-step learning decay strategy with a cosine annealing scheduling learning strategy, which optimizes the training process of the model. Finally, the performance of the method is verified on the publicly available PCB defect dataset from the Open Laboratory of Intelligent Robotics, Peking University. The experimental results show that the algorithm improves the mAP value by 3.2% compared with the original algorithm, while the fastest detection speed reaches 36.9 FPS, which can effectively improve the defect detection performance of PCB.KeywordsDefect detectionRetinaNetChannel attention

The detection of PCB defects plays an important role in PCB production. To meet the requested quality standard, systematic research was invested in PCB defect detection. The existing PCB defect detection methods are mainly trained by using an artificial defect image dataset, which is much more ideal than the real PCB production process. To deal with the real PCB production defect detection, we collected 3239 image samples and labeled each of them by label-image tool. This paper proposed a new model improved from the basic YOLOv5 framework, adding a scale and self-attention mechanism. The results revealed that this model performed well on defect location and classification with its mAP0.5 reaching 63.4%.

Printed circuit board PCB defect detection is crucial for ensuring PCB quality. However, small defect sizes on PCBs and the substantial parameter and computational requirements of deep learning models pose challenges in detection accuracy and deployment on resource-constrained devices. To address these issues, we introduce an optimized PCB defect detection method based on the RT-DETR model. Firstly, we propose the ContextAdown-D-LKAEfficientFormerV2 lightweight network to replace the ResNet18 backbone, reducing parameters and computations while enhancing small defect feature extraction. Secondly, we present the Bi-Slim-Neck lightweight structure to replace the CCFM component in the original model, achieving lightweight design and improved feature fusion capabilities to leverage effective features fully. Lastly, we propose the InnerShapeIoU loss function to replace the GIoU loss function, accounting for the influence of PCB defect bounding box shapes and scales on regression, and generating auxiliary bounding boxes suitable for PCB defect detection tasks and detectors. This enhances model generalization and detection accuracy. Experi mental results show that the improved model achieves detection accuracies of 97 mAP50 and 55 mAP50:95 , with a 23.8 reduction in parameters and a 44.9 decrease in computations compared to the original model. Thisindicates that the improved method significantly boosts parameter efficiency and reduces computational complexity while maintaining high detection accuracy.

PCB defect detection aims to identify the presence of gaps, open circuits, short circuits, and other defects in the PCB boards produced in the industry. Designing effective deep learning algorithms is crucial to finding a solution. Previously proposed PCB defect detection algorithms are limited in detecting tiny objects in high-density. Directly applying previous models to tackle PCB defect detection tasks will cause serious issues, such as missed detection and false detection. In this paper, we present a detection algorithm for tiny PCB defect targets in high-density regions to solve the above-mentioned problems. We firstly propose a detection head to detect tiny objects. Then, we design a four-channel feature fusion mechanism to fuse four different scale features and add an attention mechanism to find the attention region in scenarios with dense objects. Finally, we achieved accurate detection of tiny targets in high-density areas. Experiments were performed on the publicly available PCB defect dataset from Peking University. Our mAP@.5:.95 achieves 48.6%, while mAP@0.5 exceeds 90%. Compared with YOLOX and YOLOv5, our improved model can better localize tiny objects in high-density scenes. The experimental results certify that our model can obtain higher performance in comparison with the baseline and the state of the art.

In PCB defect detection, traditional methods often face challenges such as high manual labor costs and slow processing speeds. This paper introduces an improved approach using the YOLOv5 model, which combines the latest developments in target detection and deep learning. The YOLOv5 model stands out by providing a simpler implementation framework, improved detection performance, and accelerated processing capabilities, especially excelling in detecting small-scale targets. The paper extensively discusses the PCB manufacturing process, emphasizing the importance of reliable component encapsulation and external circuit connectivity. Furthermore, it conducts a comprehensive analysis of the YOLOv5 model structure, highlighting its operational simplicity, compact model size, and clear structural layout, which have played a crucial role in its widespread adoption in industrial applications. Finally, through extensive experimental analysis, the paper validates that the optimized YOLOv5 model achieves an average precision mean of 98.2% in PCB defect detection, a 0.6% improvement compared to the original model. These results demonstrate that the improved YOLOv5 model is an effective solution for PCB defect detection, laying the foundation for its practical implementation in modern manufacturing processes.

With the development of integrated circuit packaging technology, the layout of printed circuit boards has become complicated. Moreover, the traditional defect detection methods have been difficult to meet the requirements of high precision. Therefore, in order to solve the problem of low efficiency in defect detection of printed circuit boards, a defect detection method based on pseudo-inverse transform and improved YOLOv5 is proposed. Firstly, a defect image restoration model is constructed to improve image clarity. Secondly, Transformer is introduced to improve YOLOv5, and the batch normalization and network loss function are optimized. These methods improve the speed and accuracy of PCB defect detection. Experimental verification showed that the restoration speed of the image restoration model was 37.60%-42.38% higher than other methods. Compared with other models, the proposed PCB defect detection model had an average increase of 10.90% in recall and 12.87% in average detection accuracy. The average detection accuracy of six types of defects in the self-made PCB data set was over 98.52%, and the average detection accuracy was as high as 99.1%. The results demonstrate that the proposed method can enhance the quality of image processing and optimize YOLOv5 to improve the accuracy of detecting defects in printed circuit boards. This method is demonstrably more effective than existing technology, offering significant value and potential for application in industrial contexts. Its promotion could facilitate the advancement of industrial automation manufacturing.

With the development of integrated circuit packaging technology, the layout of printed circuit boards has become complicated. Moreover, the traditional defect detection methods have been difficult to meet the requirements of high precision. Therefore, in order to solve the problem of low efficiency in defect detection of printed circuit boards, a defect detection method based on pseudo-inverse transform and improved YOLOv5 is proposed. Firstly, a defect image restoration model is constructed to improve image clarity. Secondly, Transformer is introduced to improve YOLOv5, and the batch normalization and network loss function are optimized. These methods improve the speed and accuracy of PCB defect detection. Experimental verification showed that the restoration speed of the image restoration model was 37.60%-42.38% higher than other methods. Compared with other models, the proposed PCB defect detection model had an average increase of 10.90% in recall and 12.87% in average detection accuracy. The average detection accuracy of six types of defects in the self-made PCB data set was over 98.52%, and the average detection accuracy was as high as 99.1%. The results demonstrate that the proposed method can enhance the quality of image processing and optimize YOLOv5 to improve the accuracy of detecting defects in printed circuit boards. This method is demonstrably more effective than existing technology, offering significant value and potential for application in industrial contexts. Its promotion could facilitate the advancement of industrial automation manufacturing.

PCB defect detection requires both high accuracy and real-time performance, while small defect sizes and complex backgrounds pose significant challenges for deployment on edge devices. This paper proposes a real-time PCB defect detection method based on the YOLOv5n model and constructs an edge-side detection system on Raspberry Pi 5. To enhance small defect detection capability, dataset integration, image slicing, and data augmentation strategies are employed. The trained model is deployed using the NCNN framework for lightweight inference. Experimental results demonstrate that the proposed method achieves 97.0% mAP50 on the test set and maintains a stable real-time detection speed of approximately 9 FPS on Raspberry Pi 5, showing strong performance on small-scale and sparse defects. The proposed approach provides a feasible solution for low-cost PCB defect detection on edge devices.

When the object detection algorithm faces the chanllenge of PCB defect detection, we need to consider the speed and accuracy of detection under the condition of limited computing resources. This paper provides an improved object detection algorithm to enhance the detection efficiency of small objects with defects and achieves efficient model lightweighting with little loss of detection accuracy. First, GSConv and VoV-GSCSP networks are introduced to replace the original Conv and C3 network modules in the feature extraction part and add an attention mechanism module to enhance the dimension of information and reduce redundant information candidate boxes. The attention module of the Shuffle backbone network is used to extract information effectively without losing too much information. The number of invalid extracted features is reduced by improving the feature extraction capability of the backbone network and the effectiveness of feature fusion. The final experimental results show that the improved lightweight model in this paper reduces the weight of the yolov5s6 model size by 51% on the PCB defect detection dataset published by Peking University. Our proposed method can improve the model's mAP.5 by 2-3%. For defect detection, the small object recognition effect is significantly improved, and the lightweight effect is remarkable.

With the rapid advancement of the electronics manufacturing industry, the quality requirements for circuit board production continue to increase. To effectively reduce product defect rates, PCB appearance defect detection has become critical in industrial production. Initially, PCB defect inspection relied on manual visual checks. However, with the increasing integration of electronic devices, this manual approach can no longer meet the practical demands of industrial production. To address these challenges, this study proposes a computer vision-based algorithm for detecting product appearance defects and develops a user-friendly PCB defect detection system using Python and PyQt5. First, we examined the characteristics and detection requirements of PCB board appearance defect detection. We analyzed the features of defects that may arise during PCB board production, collected existing datasets, and analyzed the current challenges in PCB board appearance defect detection. We designed the overall structural framework of the system solution and the detection workflow for the defect detection algorithm. Second, we selected the YOLOv8 deep learning model, which excels in the field of object detection due to its efficient detection speed and high detection accuracy. We trained the YOLOv8 model using the dataset, adjusting model parameters and optimizing algorithms to achieve optimal detection performance. Following training, we evaluated the model's performance metrics, including detection accuracy and recall rate. Based on the evaluation results, we implement necessary optimizations to enhance the model's generalization capability and detection performance. Finally, we integrate the trained YOLOv8 model into the PCB board defect detection system and conduct comprehensive testing to ensure the system's stability and reliability in actual production environments. The improved YOLOv8_SE achieved 89.1% mAP and 52.3 FPS, outperforming prior models.

In view of the low robustness of the existing traditional PCB defect detection algorithms, this paper applies a PCB defect detection and recognition algorithm based on deep convolutional nerual network framework SSD(Single Shot Detector). This algorithm structure utilizes multi-scale feature maps to customise boundary boxes with different scales, and applies small convolution kernel (3*3)to predict the classification results and boundary box information. Then the detection results gracefully optimize by non-maximum suppression (NMS). Finally, in order to prove the superiority of this algorithm, this paper conducts comparative experiments. The experimental results show that the algorithm has a significant improvement in the accuracy of PCB defect detection, and the identification accuracy of PCB nodules can be as high as 94.69%. It has good applicability in the application of PCB defect detection.

This study looks into ways to handle multi-scale feature blurring, edge detail loss, and inaccurate localization in PCB defect detection. We introduce a Dynamic Multi-dimensional Hybrid Attention (DMHA) mechanism and build the DMHA-YOLO network based on the YOLOv12 framework. The DMHA mechanism mainly consists of three components: first, a hierarchical adaptive dynamic channel compression module that optimizes the expression of multi-scale defect features; second, an edge-enhanced spatial perception module with learnable weights that helps reduce detail loss; and third, a coordinate positional attention module with explicit row-column encoding that improves the localization of micro-defects. These components are put together in a cascade-residual design to allow interaction across channel, spatial, and positional dimensions. Evaluations on the Peking University PCB_DATASET reveal that DMHA-YOLO attains a 3% enhancement (from 91.1% to 94.1%) in detection accuracy and a 3.9% improvement (from 50.4% to 54.3% ) in mAP@50-95 compared to the baseline YOLOv12. When compared with modified models that use attention mechanisms like CBAM, CoordAtt, and ECA in the same framework, DMHA-YOLO achieves higher recall and better localization at different scales without increasing computation. Our experiments show that the method is effective and stable for PCB defect detection, and it a good choice for accurate quality checks in factories.