Using deep learning to automatic inspection system of printed circuit board in manufacturing industry under the internet of things

Zhang, Kai

doi:10.2298/csis220718020z

Cited by 3 publications

(2 citation statements)

References 39 publications

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“…Zhang et al [20] just used YOLOv3 to recognize PCB components without any modification. The overall detection rate for 11 types of PCB components was about 92%, and the running time was 0.55s for each input image.…”

Section: B Deep Learning Based Methods For Pcb Inspectionmentioning

confidence: 99%

Precise Detection for Dense PCB Components Based on Modified YOLOv8

Ling,

Isa,

Asaari

2023

IEEE Access

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Effective detection of dense printed circuit board (PCB) components contributes to the optimization of automatic flow of production. In addition, PCB component recognition is also the essential prerequisite for early defect detection. Current PCB component detection approaches are not adept in both rapid and precise detection. YOLOv8 models have exhibited effective performances for detecting common objects, such as person, car, chair, dog etc. However, it is still tricky for YOLOv8 models to inspect dense and disparate PCB components precisely. Thus, a novel convolution neural network (CNN) model is proposed for dense PCB component detection by introducing several modifications onto YOLOv8. First, creative C2Focal module is designed as the core element of the backbone, combining both fine-grained local and coarse-grained global features concurrently. Then, the lightweight Ghost convolutions are inserted to effectively reduce the computation cost, meanwhile maintaining the detection performance. Finally, a new bounding box regression loss that is Sig-IoU loss, is proposed to facilitate the prediction regression and promote the positioning accuracy. The experiments on our PCB component dataset demonstrate that our proposed model performs the highest mean average precisions of 87.7%

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Section: B Deep Learning Based Methods For Pcb Inspectionmentioning

confidence: 99%

Precise Detection for Dense PCB Components Based on Modified YOLOv8

Ling,

Isa,

Asaari

2023

IEEE Access

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“…Zhang [47] proposes a method for automatic inspection of PCBs that comprises YOLO deep learning algorithm, image processing technique and an algorithm for position correction. [48] are developed the electronic component localization and detection network (ECLAD-Net) for identification and classification of resistors and capacitors on a PCB and in this way to detect malicious and reused components.…”

Section: Visual Inspection At Pcbs and Pcbamentioning

confidence: 99%

Printed circuit board and printed circuit board assembly methods for testing and visual inspection: a review

Petkov,

Ivanova

2024

Bulletin EEI

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Testing and visual inspection of printed circuit boards (PCBs) and printed circuit board assemblies (PCBAs) are important procedures in the manufacturing process of electronic modules and devices related to locating and identifying possible defects and failures. Earlier defects detection leads to decreasing expenses, time and used resources to produce high quality electronics. In this paper an exploration and analysis about the current research regarding methods for PCB and PCBA testing, techniques for defects detection and vusial inspection is performed. The impact of machine and deep learning for testing and visual inspection procedures is also investigated. The used methodology comprises bibliometric approach and content analysis of papers, indexed in scientific database Scopus, considering the queries: “PCB and testing” and “PCB and testing”, “printed circuit board assembly and testing” and “PCBA and testing”, “PCB defect detection” and “PCBA defect detection”, “PCB and visual inspection”, and “PCBA and visual inspection”. The findings are presented in the form of a framework, which summarizes the contemporary landscape of methods for PCBs and PCBAs testing and visual inspection.

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EC-YOLO: Improved YOLOv7 Model for PCB Electronic Component Detection

Luo,

Wan,

Lei

et al. 2024

Sensors

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Electronic components are the main components of PCBs (printed circuit boards), so the detection and classification of ECs (electronic components) is an important aspect of recycling used PCBs. However, due to the variety and quantity of ECs, traditional target detection methods for EC classification still have problems such as slow detection speed and low performance, and the accuracy of the detection needs to be improved. To overcome these limitations, this study proposes an enhanced YOLO (you only look once) network (EC-YOLOv7) for detecting EC targets. The network uses ACmix (a mixed model that enjoys the benefits of both self-attention and convolution) as a substitute for the 3 × 3 convolutional modules in the E-ELAN (Extended ELAN) architecture and implements branch links and 1 × 1 convolutional arrays between the ACmix modules to improve the speed of feature retrieval and network inference. Furthermore, the ResNet-ACmix module is engineered to prevent the leakage of function data and to minimise calculation time. Subsequently, the SPPCSPS (spatial pyramid pooling connected spatial pyramid convolution) block has been improved by replacing the serial channels with concurrent channels, which improves the fusion speed of the image features. To effectively capture spatial information and improve detection accuracy, the DyHead (the dynamic head) is utilised to enhance the model’s size, mission, and sense of space, which effectively captures spatial information and improves the detection accuracy. A new bounding-box loss regression method, the WIoU-Soft-NMS method, is finally suggested to facilitate prediction regression and improve the localisation accuracy. The experimental results demonstrate that the enhanced YOLOv7 net surpasses the initial YOLOv7 model and other common EC detection methods. The proposed EC-YOLOv7 network reaches a mean accuracy (mAP@0.5) of 94.4% on the PCB dataset and exhibits higher FPS compared to the original YOLOv7 model. In conclusion, it can significantly enhance high-density EC target recognition.

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Using deep learning to automatic inspection system of printed circuit board in manufacturing industry under the internet of things

Cited by 3 publications

References 39 publications

Precise Detection for Dense PCB Components Based on Modified YOLOv8

Precise Detection for Dense PCB Components Based on Modified YOLOv8

Printed circuit board and printed circuit board assembly methods for testing and visual inspection: a review

EC-YOLO: Improved YOLOv7 Model for PCB Electronic Component Detection

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