YOLO-RLC: An Advanced Target-Detection Algorithm for Surface Defects of Printed Circuit Boards Based on YOLOv5
Source abstract
Abstract
Printed circuit boards (PCBs) provide stable connections between electronic components. However, defective printed circuit boards may cause the entire equipment system to malfunction, resulting in incalculable losses. Therefo... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Integrated Circuits and Semiconductor Failure Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (51)
Show source-provided references
1. Algorithmic scheme for concurrent detection and classification of printed circuit board defects
2. Automatic PCB sample generation and defect detection based on ControlNet and swin transformer
3. Evaluation of pooling operations in convolutional architec-tures for object recognition
4. An interdigital electrode probe for detection, localization and evaluation of surface notch-type damage in metals
5. Precision improvement of deep learning algorithm for PCB defect detection
6. A surface defect detection algorithm for PCB based on MobileViT-YOLO
7. A fast detection algorithm for surface defects of bare PCB based on YOLOv8
8. Rapid detection of PCB defects based on YOLOx-Plus and FPGA10.1109/ACCESS.2024.3387947 ↗
9. A review of deep learning based anomaly detection strategies in Industry 4.0 focused on application fields, sensing equipment and algorithms10.1109/ACCESS.2024.3424488 ↗
10. Enhancing defect prediction with static-defect analysis
11. A non-invasive technique for online defect detection on steel strip surfaces
12. Defect detection using two-dimensional moving range filter and unanimous vote among color component classifiers
13. Fabric defect detection based on cascade faster R-CNN
14. Small object detection method with shallow feature fusion network for chip surface defect detection
15. Cross-dimension feature attention aggregation network for cloud and snow recognition of high satellite images10.1007/s00521-024-09477-5 ↗
16. Aluminum surface defect detect-ion method based on a lightweight YOLOv4 network
17. Fabric defect detection using fully convolutional network with attention mechanism
18. Defect detection for mobile phone cases based on improved yolo model
19. Overview of temporal action detection based on deep learning
20. Y. Zang, J. Zhang, and M. M. Billah, “Defect detection of flexible circuit board based on convolutional neural network,” presented at 2nd Int. Conf. Comput. Data Sci., Nov. 1–5, 2021.10.1145/3448734.3450927 ↗
21. Scaling up your kernels to 31 × 31: Revisiting large kernel design in CNNs
22. EfficientDet: Scalable and efficient object detection
23. RepVGG: Making VGG-style ConvNets great again
24. Vision transformer with super token sampling
25. Xception: Deep learning with depthwise separable convolutions
26. Group normalization
27. Deep residual learning for image recognition
28. Enhancing geometric factors in model learning and inference for object detection and instance segmentation10.1109/TCYB.2021.3095305 ↗
29. Focal and efficient IOU loss for accurate bounding box regression10.1016/j.neucom.2022.07.042 ↗
30. Faster R-CNN: Towards real-time object detection with region proposal networks10.1109/TPAMI.2016.2577031 ↗
31. SSD: Single shot MultiBox detector
32. Focal loss for dense object detection
33. CenterNet: Keypoint triplets for object detection
34. FCOS: A simple and strong anchor-free object detector
35. Object detection algorithm based on improved YOLOv310.3390/electronics9030537 ↗
36. Face mask wearing detection algorithm based on improved YOLO-v4
37. ultralytics/yolov5: v5. 0-YOLOv5-P6 1280 models, AWS, Supervisely and YouTube integrations
38. C. Li et al., “YOLOv6: A single-stage object detection framework for industrial applications,” 2022, arXiv:2209.02976.
39. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
40. YOLOv1 to v8: Unveiling each variant—A comprehensive review of YOLO10.1109/ACCESS.2024.3378568 ↗
41. C. Y. Wang, I. H. Yeh, and H. Y. M. Liao, “YOLOv9: Learning what you want to learn using programmable gradient information,” 2024, arXiv:2402.13616.10.1007/978-3-031-72751-1_1 ↗
42. A. Wang et al., “YOLOv10: Real-time end-to-end object detection,” 2024, arXiv:2405.14458.
43. M. Ye, H. Wang, and H. Xiao, “Light-YOLOv5: A lightweight algorithm for improved YOLOv5 in PCB defect detection,” presented at 2023 IEEE 2nd Int. Conf. Electr. Eng., Big Data Algorithms, IEEE, Feb. 2023, pp. 523–528. doi: 10.1109/EEBDA56825.2023.10090731.10.1109/EEBDA56825.2023.10090731 ↗
44. Research on PCB small target defect detection based on improved YOLOv5
45. PCB-YOLO: An improved detection algorithm of PCB surface defects based on YOLOv5
46. Global contextual attention augmented YOLO with ConvMixer prediction heads for PCB surface defect detection10.1038/s41598-023-36854-2 ↗
47. RDD-YOLO: A modified YOLO for detection of steel surface defects10.1016/j.measurement.2023.112776 ↗
48. DCAM-Net: A rapid detection network for strip steel surface defects based on deformable convolution and attention mechanism
49. An infrared image defect detection method for steel based on regularized YOLO10.3390/s24051674 ↗
50. Steel surface defect detection method based on improved YOLOX10.1109/ACCESS.2024.3374869 ↗
51. Fine-YOLO: A simplified X-ray prohibited object detection network based on feature aggre-gation and normalized wasserstein distance