Evaluation of Lightweight Object Detection and Instance Segmentation Techniques for Industrial Printed Circuit Board Solder Quality Assessment

Yıl 2025, Cilt: 21 Sayı: 4, 128 - 138, 29.12.2025

Tuncay Soylu , Emel Soylu

https://doi.org/10.18466/cbayarfbe.1669378

Öz

The rapid progress of deep learning has transformed the detection of soldering defects in printed circuit boards (PCBs), outperforming traditional manual inspections and rule-based machine vision systems.This study evaluates the performance of state-of-the-art YOLO models—specifically YOLOv11 and YOLOv12—for both object detection and instance segmentation of solder defects in aerospace PCBs using the open-source SolDef_AI dataset. We compare multiple variants (nano, small, and medium) to assess their accuracy, efficiency, and suitability for industrial quality control. Our experiments reveal that YOLO-v11s-seg achieves the highest mean average precision (mAP50-95: 0.853 for detection, 0.822 for segmentation), demonstrating superior defect localization capabilities, particularly for challenging classes such as "poor_solder" and "spike." While YOLOv12 models exhibit competitive detection performance, they show slightly lower segmentation accuracy, indicating potential areas for architectural refinement. Smaller models (YOLO-v11n, YOLO-v12n) offer a favorable balance between speed and precision, making them viable for real-time applications. The findings highlight the effectiveness of YOLO-based deep learning in automating solder defect inspection, with implications for improving manufacturing quality assurance in electronics production.

Anahtar Kelimeler

Image Processing , Object Detection , Quality Control , Soldering Defect Detection

Kaynakça

• [1]. W. Dai, A. Mujeeb, M. Erdt, and A. Sourin, “Soldering defect detection in automatic optical inspection,” Adv. Eng. Informatics, vol. 43, p. 101004, 2020.
• [2]. Q. Ling, N. A. M. Isa, and M. S. M. Asaari, “SDD-Net: Soldering defect detection network for printed circuit boards,” Neurocomputing, vol. 610, p. 128575, 2024.
• [3]. A. Sezer and A. Altan, “Detection of solder paste defects with an optimization-based deep learning model using image processing techniques,” Solder. \& Surf. Mt. Technol., vol. 33, no. 5, pp. 291–298, 2021.
• [4]. A. Bhattacharya and S. G. Cloutier, “End-to-End Deep Learning Framework for Printed Circuit Board Manufacturing Defect Classification,” Sci. Rep., 2022.
• [5]. Y. Wang et al., “Surface Defect Detection of Printed Circuit Board With Large Kernel Convolutional Networks,” 2024.
• [6]. E. Vakili, G. Karimian, M. Shoaran, R. Yadipour, and J. Sobhi, “Valid-IoU: An Improved IoU-based Loss Function and Its Application to Detection of Defects on Printed Circuit Boards,” Multimed. Tools Appl., 2024.
• [7]. W. Chen, H. Zhao, and Z. Wang, “Defect Detection Model of Printed Circuit Board Components Based on the Fusion of Multi-Scale Features and Efficient Channel Attention Mechanism,” Ieee Access, 2024.
• [8]. Y. Liu, H. Wu, Y. Xu, X. Liu, and X. Yu, “Automatic PCB Sample Generation and Defect Detection Based on ControlNet and Swin Transformer,” Sensors, 2024.
• [9]. S. Liang et al., “Edge YOLO: Real-Time Intelligent Object Detection System Based on Edge-Cloud Cooperation in Autonomous Vehicles,” Ieee Trans. Intell. Transp. Syst., 2022.
• [10]. G. Lavanya and S. D. Pande, “Enhancing Real-Time Object Detection With YOLO Algorithm,” Eai Endorsed Trans. Internet Things, 2023.
• [11]. M. J. Shaifee, B. Chywl, F. Li, and A. Wong, “Fast YOLO: A Fast You Only Look Once System for Real-Time Embedded Object Detection in Video,” J. Comput. Vis. Imaging Syst., 2017.
• [12]. Z. Guo, C. Wang, G. Yang, Z. Huang, and G. Li, “MSFT-YOLO: Improved YOLOv5 Based on Transformer for Detecting Defects of Steel Surface,” Sensors, 2022.
• [13]. W. Tao, “Analysis the Improvements of YOLOv5 Algorithms: NRT-YOLO, MR-YOLO and YPH-YOLOv5,” Appl. Comput. Eng., 2024.
• [14]. X. Lang, Z. Ren, D. Wan, Y. Zhang, and S. Shu, “MR-YOLO: An Improved YOLOv5 Network for Detecting Magnetic Ring Surface Defects,” Sensors, 2022.
• [15]. J. Terven, D. Córdova‐Esparza, and J.-A. Romero-González, “A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS,” Mach. Learn. Knowl. Extr., 2023.
• [16]. M. Hussain, “YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature Toward Digital Manufacturing and Industrial Defect Detection,” Machines, 2023.
• [17]. L. Lü, X. Li, Y. Wu, and B. Chen, “Enhanced RT-DETR for Traffic Sign Detection:Small Object Precision and Lightweight Design,” 2024.
• [18]. Z. Li et al., “RT-DETR-SoilCuc: Detection Method for Cucumber Germinationinsoil Based Environment,” Front. Plant Sci., 2024.
• [19]. S. Tang, “Improvement of RT-DETR Model for Ground Glass Pulmonary Nodule Detection,” PLoS One, 2025.
• [20]. C. Tang, Y. Li, L. Wang, and W. Li, “Real-Time Traffic Light Detection Based on Lightweight Improved RT-DETR,” 2024.
• [21]. G. Fontana, M. Calabrese, L. Agnusdei, G. Papadia, and A. Del Prete, “SolDef_AI: An Open Source PCB Dataset for Mask R-CNN Defect Detection in Soldering Processes of Electronic Components,” J. Manuf. Mater. Process., vol. 8, no. 3, p. 117, 2024.
• [22]. K. Zhang and H. Shen, “Solder Joint Defect Detection in the Connectors Using Improved Faster-RCNN Algorithm,” Appl. Sci., 2021.
• [23]. J. P. R Nayak and B. D. Parameshachari, “Effective PCB Defect Detection Using Stacked Autoencoder With Bi-LSTM Network,” Int. J. Intell. Eng. Syst., 2022.
• [24]. X. Liao, S. Lv, D. Li, Y. Luo, Z. Zhu, and C. Jiang, “YOLOv4-MN3 for PCB Surface Defect Detection,” Appl. Sci., 2021.
• [25]. W. Huang and P. Wei, “A PCB dataset for defects detection and classification,” arXiv Prepr. arXiv1901.08204, 2019.
• [26]. “SolDef_AI: PCB dataset for defect detection.” [Online]. Available: https://kaggle.com/datasets/f899d21ce26435a9aa74da20a1409641fcafee386bdaf980e451cdbd4d744e0c.
• [27]. R. Khanam and M. Hussain, “Yolov11: An overview of the key architectural enhancements,” arXiv Prepr. arXiv2410.17725, 2024.
• [28]. Y. Tian, Q. Ye, and D. Doermann, “Yolov12: Attention-centric real-time object detectors,” arXiv Prepr. arXiv2502.12524, 2025.
• [29]. R. Padilla, S. L. Netto, and E. A. B. Da Silva, “A survey on performance metrics for object-detection algorithms,” in 2020 international conference on systems, signals and image processing (IWSSIP), 2020, pp. 237–242.
• [30]. S. Raschka, “An overview of general performance metrics of binary classifier systems,” arXiv Prepr. arXiv1410.5330, 2014.
• [31]. J. P. R. Nayak and B. D. Parameshachari, “Effective PCB Defect Detection Using Stacked Autoencoder With Bi-LSTM Network,” Int. J. Intell. Eng. Syst., vol. 15, no. 2, pp. 285–294, 2022.
• [32]. Y. Xu and others, “Lightweight PCB Solder Joint Defect Detection with YOLO11n Enhanced by RetBlock, SAF, and AAF,” J. Intell. Manuf., vol., 2025.
• [33]. H. Li, others, I. Mendizabal-Arrieta, and others, “SCF-YOLO: A Lightweight Real-Time Model for PCB Surface Defect Detection,” Comput. Ind., vol., p. , 2025.
• [34]. Q. Zhu and others, “VR-YOLO: Viewpoint-Robust PCB Defect Detection Based on YOLOv8 with CBAM,” IEEE Access, vol., p. , 2025.