Enhanced YOLOv8 with BiFPN-SimAM for Precise Defect Detection in Miniature Capacitors
Source abstract
Abstract
In the domain of automatic visual inspection for miniature capacitor quality control, the task of accurately detecting defects presents a formidable challenge. This challenge stems primarily from the small size and limited sample availability of defective micro-capacitors, which leads to issues such as reduced detection accuracy and increased false-negative rates in existing inspection methods. To address these challenges, this paper proposes an innovative approach employing an enhanced ‘you only look once’ version 8 (YOLOv8) architecture specifically tailored for the intricate task of micro-capacitor defect inspection. The merging of the bidirectional feature pyramid network (BiFPN) architecture and the simplified attention module (SimAM), which greatly improves the model’s capacity to recognize fine features and feature representation, is at the heart of this methodology. Furthermore, the model’s capacity for generalization was significantly improved by the addition of the weighted intersection over union (WISE-IOU) loss function. A micro-capacitor surface defect (MCSD) dataset comprising 1358 images representing four distinct types of micro-capacitor defects was constructed. The experimental results showed that our approach achieved 95.8% effectiveness in the mean average precision (mAP) at a threshold of 0.5. This indicates a notable 9.5% enhancement over the original YOLOv8 architecture and underscores the effectiveness of our approach in the automatic visual inspection of miniature capacitors.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Advanced Neural Network Applications · Non-Destructive Testing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Yibin Park of University of Electronic Science and Technology of China, Yibin 644000, China
Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518000, China
Funding
National Natural Science Foundation of China
References (45)
Show source-provided references
1. Liao, Y., Najafi-Haghi, Z.P., Wunderlich, H.-J., and Yang, B. (2022, January 23–30). Efficient and Robust Resistive Open Defect Detection Based on Un-supervised Deep Learning. Proceedings of the 2022 IEEE International Test Conference (ITC), Anaheim, CA, USA.10.1109/ITC50671.2022.00026 ↗
2. Review of vision-based defect detection research and its perspectives for printed circuit board10.1016/j.jmsy.2023.08.019 ↗
3. Liu, B., Wang, H., Wang, Y., Zhou, C., and Cai, L. (2023). Lane Line Type Recognition Based on Improved YOLOv5. Appl. Sci., 13.10.3390/app131810537 ↗
4. Review of condition monitoring methods for capacitors used in power converters10.1016/j.microrel.2023.115003 ↗
5. Deep Learning in Computer Vision: A Critical Review of Emerging Techniques and Ap-plication Scenarios
6. Artificial intelligence, machine learning and deep learning in advanced robotics, a review10.1016/j.cogr.2023.04.001 ↗
7. Comparative analysis of deep learning image detection algorithms10.1186/s40537-021-00434-w ↗
8. Zhang, Q., Zhang, H., and Lu, X. (2022). Adaptive Feature Fusion for Small Object Detection. Appl. Sci., 12.10.3390/app122211854 ↗
9. Mao, R., Wang, Z., Li, F., Zhou, J., Chen, Y., and Hu, X. (2023). GSEYOLOX-s: An Improved Lightweight Network for Identifying the Severity of Wheat Fusarium Head Blight. Agronomy, 13.10.3390/agronomy13010242 ↗
10. Yu, X., Yu, Q., Mu, Q., Hu, Z., and Xie, J. (2023). MCAW-YOLO: An Efficient Detection Model for Ceramic Tile Surface Defects. Appl. Sci., 13.10.3390/app132112057 ↗
11. Focal and efficient IOU loss for accurate bounding box regression10.1016/j.neucom.2022.07.042 ↗
12. Tong, Z., Chen, Y., Xu, Z., and Yu, R. (2023). Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism. arXiv.
13. Tian, B., and Chen, H. (2023). Remote Sensing Image Target Detection Method Based on Refined Feature Extraction. Appl. Sci., 13.10.3390/app13158694 ↗
14. Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A Survey10.1109/ACCESS.2023.3271748 ↗
15. Image-Based Surface Defect Detection Using Deep Learning: A Review10.1115/1.4049535 ↗
16. Faster-YOLO: An accurate and faster object detection method10.1016/j.dsp.2020.102756 ↗
17. Improving the network architecture of YOLOv7 to achieve real-time grading of canola based on kernel health10.1016/j.atech.2023.100300 ↗
18. Li, G., Zhao, S., Zhou, M., Li, M., Shao, R., Zhang, Z., and Han, D. (2022). YOLO-RFF: An Industrial Defect Detection Method Based on Expanded Field of Feeling and Feature Fusion. Electronics, 11.10.3390/electronics11244211 ↗
19. Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8–14). Cbam: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01234-2_1 ↗
20. A Coarse-Grained Regularization Method of Convolutional Kernel for Molten Pool Defect Identification10.1115/1.4045294 ↗
21. Zhang, J., Zhou, H., Niu, Y., Lv, J., Chen, J., and Cheng, Y. (2021). CNN and multi-feature extraction based denoising of CT images. Biomed. Signal Process. Control, 67.10.1016/j.bspc.2021.102545 ↗
22. A review on the attention mechanism of deep learning10.1016/j.neucom.2021.03.091 ↗
23. Wang, D., Wang, X., Wang, L., Li, M., Da, Q., Liu, X., Gao, X., Shen, J., He, J., and Shen, T. (2023). MedFMC: A Real-World Dataset and Benchmark for Foundation Model Adaptation in Medical Image Classification. arXiv.10.1038/s41597-023-02460-0 ↗
24. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
25. Yang, L., Zhang, R.-Y., Li, L., and Xie, X. (2021, January 18–24). Simam: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks. Proceedings of the 38th International Conference on Machine Learning, PMLR, Virtual.
26. Qian, J., Lin, J., Bai, D., Xu, R., and Lin, H. (2023). Omni-Dimensional Dynamic Convolution Meets Bottleneck Transformer: A Novel Improved High Accuracy Forest Fire Smoke Detection Model. Forests, 14.10.3390/f14040838 ↗
27. Tian, Z., Yang, F., and Qin, D. (2023). An Improved New YOLOv7 Algorithm for Detecting Building Air Conditioner External Units from Street View Images. Sensors, 23.10.3390/s23229118 ↗
28. Bai, W., Zhao, J., Dai, C., Zhang, H., Zhao, L., Ji, Z., and Ganchev, I. (2023). Two Novel Models for Traffic Sign Detection Based on YOLOv5s. Axioms, 12.10.3390/axioms12020160 ↗
29. Li, J., Tian, Y., Chen, J., and Wang, H. (2023). Rock Crack Recognition Technology Based on Deep Learning. Sensors, 23.10.3390/s23125421 ↗
30. Zhang, Y., and Ni, Q. (2023). A Novel Weld-Seam Defect Detection Algorithm Based on the S-YOLO Model. Axioms, 12.10.3390/axioms12070697 ↗
31. An improved YOLOv5 method for large objects detection with multi-scale feature cross-layer fusion network10.1016/j.imavis.2022.104518 ↗
32. Chiley, V., Thangarasa, V., Gupta, A., Samar, A., Hestness, J., and De Coste, D. (2023). RevBiFPN: The Fully Reversible Bidirectional Feature Pyramid Network. Proceedings of Machine Learning and Systems. arXiv.
33. Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation10.1109/TCYB.2021.3095305 ↗
34. Cho, Y.-J. (2023). Weighted Intersection over Union (wIoU): A New Evaluation Metric for Image Segmentation. arXiv.
35. Li, L., Yang, L., and Zeng, Y. (2021). Improving Sentiment Classification of Restaurant Reviews with Attention-Based Bi-GRU Neural Network. Symmetry, 13.10.3390/sym13081517 ↗
36. Luo, M., Liu, X., and Huang, W. (2019, January 20–22). Gaze Estimation Based on Neural Network. Proceedings of the 2019 IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT), Harbin, China.10.1109/ICEICT.2019.8846281 ↗
37. Xiao, Z., Wan, F., Lei, G., Xiong, Y., Xu, L., Ye, Z., Liu, W., Zhou, W., and Xu, C. (2023). FL-YOLOv7: A Lightweight Small Object Detection Algorithm in Forest Fire Detection. Forests, 14.10.3390/f14091812 ↗
38. Lei, F., Tang, F., and Li, S. (2022). Underwater Target Detection Algorithm Based on Improved YOLOv5. J. Mar. Sci. Eng., 10.10.3390/jmse10030310 ↗
39. SSD: Single Shot MultiBox Detector
40. Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7–12). Faster R-CNN: Towards real-time object detection with region proposal networks. Proceedings of the Advances in Neural Information Processing Systems 28 (NIPS 2015), Montreal, QC, Canada.
41. Improved YOLOv5 network for real-time multi-scale traffic sign detection10.1007/s00521-022-08077-5 ↗
42. Wang, C., He, W., Nie, Y., Guo, J., Liu, C., Han, K., and Wang, Y. (2023). Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism. arXiv.
43. Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y.M. (2023, January 17–24). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.10.1109/CVPR52729.2023.00721 ↗
44. Reis, D., Kupec, J., Hong, J., and Daoudi, A. (2023). Real-Time Flying Object Detection with YOLOv8. arXiv.
45. Zhu, X., Lyu, S., Wang, X., and Zhao, Q. (2021). TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios. arXiv.10.1109/ICCVW54120.2021.00312 ↗