The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
A Small-Sized Object Detection Oriented Multi-Scale Feature Fusion Approach With Application to Defect Detection
Open accessAbstract
Object detection is a well-known task in the field of computer vision, especially the small target detection problem that has aroused great academic attention. In order to improve the detection performance of small objects, in this article, a novel enhanced multiscale feature fusion method is proposed, namely, the atrous spatial pyramid pooling-balanced-feature pyramid network (ABFPN). In particular, the atrous convolution operators with different dilation rates are employed to make full use of context information, where the skip connection is applied to achieve sufficient feature fusions. In addition, there is a balanced module to integrate and enhance features at different levels. The performance of the proposed ABFPN is evaluated on three public benchmark datasets, and experimental results demonstrate that it is a reliable and efficient feature fusion method. Furthermore, in order to validate the applicational potential in small objects, the developed ABFPN is utilized to detect surface tiny defects of the printed circuit board (PCB), which acts as the neck part of an improved PCB defect detection (IPDD) framework. While designing the IPDD, several powerful strategies are also employed to further improve the overall performance, which is evaluated via extensive ablation studies. Experiments on a public PCB defect detection database have demonstrated the superiority of the designed IPDD framework against the other seven state-of-the-art methods, which further validates the practicality of the proposed ABFPN.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
Microsoft COCO: Common Objects in Context
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Cited by
Albahri, A. S., Duhaim, A. M., Fadhel, M. A., Alnoor, A., Baqer, N. S., Alzubaidi, L., Albahri, O. S., Alamoodi, A. H., Bai, J., Salhi, A., et al. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.03.008 · Cited by 716
A deep learning model for steel surface defect detection
Li, Z., Wei, X., Hassaballah, M., Li, Y., & Jiang, X. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01180-7 · Cited by 122
Logit Inducing With Abnormality Capturing for Semi-Supervised Image Anomaly Detection
Wan, Q., Gao, L., & Li, X. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3205674 · Cited by 22
Metadata
- Authors
- Zeng, N., Wu, P., Wang, Z., Li, H., Liu, W., & Liu, X.
- Venue
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2022
- Volume
- 71
- Pages
- 1-14
- ISSN
- 0018-9456 (ISSN-L)
- 1557-9662
- OpenAlex
- W4213436958
- Cited by
- 523
- References
- 60
- FWCI
- 38.20
- Type
- journal article
- Language
- en
- Open access
- green
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- Zeng, N., Wu, P., Wang, Z., Li, H., Liu, W., & Liu, X. (2022). A Small-Sized Object Detection Oriented Multi-Scale Feature Fusion Approach With Application to Defect Detection. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/tim.2022.3153997
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