Abstract
Industrial defect detection is a hot topic in the computer vision field. At the same time, it is hard work because of the complex features and various categories of industrial defects. To solve the above problem, this paper introduces a lightweight and efficient deep learning model (SSDD-Net) for steel surface defect detection. At the same time, in order to improve the efficiency of model training and inference in the XPU distributed computing environment, parallel computing is introduced in this paper. First, a light multiscale feature extraction module (LMFE) is designed to enhance the model’s ability to extract features. The LMFE module employs three branches with different receptive fields to extract multiscale features. Second, a simple effective feature fusion network (SEFF) is introduced to be the neck network of the SSDD-Net to achieve efficient feature fusion. Extensive experiments are conducted on a steel surface defect detection dataset, NEU-DET, to verify the effectiveness of the designed modules and proposed model. And the experimental results demonstrate that the designed modules are effective. Compared with other SOTA object detection models, the proposed model obtains optimal performance (73.73% in mAP@0.5) while keeping a small number of parameters (3.79M).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. nature 521(7553), 436–444 (2015)
Zou, Z., Shi, Z., Guo, Y., Ye, J.: Object detection in 20 years: a survey. arXiv preprint arXiv:1905.05055 (2019)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017)
Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOV4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)
Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Ultralytics: YOLOv5 v6.2. https://github.com/ultralytics/yolov5. Accessed 17 Aug 2022
Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv:2207.02696 (2022)
Wang, H., Li, Z., Wang, H.: Few-shot steel surface defect detection. IEEE Trans. Instrum. Meas. 71, 1–12 (2021)
Hatab, M., Malekmohamadi, H., Amira, A.: Surface defect detection using YOLO network. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) IntelliSys 2020. AISC, vol. 1250, pp. 505–515. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-55180-3_37
Wang, L., Liu, X., Ma, J., Su, W., Li, H.: Real-time steel surface defect detection with improved multi-scale YOLO-v5. Processes 11(5), 1357 (2023)
Wang, C., Xu, J., Liang, X., Yin, D.: Metal surface defect detection based on weighted fusion. In: 2020 International Conference on Virtual Reality and Visualization (ICVRV), pp. 179–184. IEEE (2020)
Deng, H., Cheng, J., Liu, T., Cheng, B., Sun, Z.: Research on iron surface crack detection algorithm based on improved YOLOv4 network. J. Phys: Conf. Ser. 1631, 012081 (2020)
Kou, X., Liu, S., Cheng, K., Qian, Y.: Development of a YOLO-v3-based model for detecting defects on steel strip surface. Measurement 182, 109454 (2021)
He, Y., Song, K., Meng, Q., Yan, Y.: An end-to-end steel surface defect detection approach via fusing multiple hierarchical features. IEEE Trans. Instrum. Meas. 69(4), 1493–1504 (2019)
Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)
Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759–8768 (2018)
Acknowledgements
This work was supported by the project ZR2022LZH017 supported by Shandong Provincial Natural Science Foundation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, Z., Wei, X., Jiang, X. (2024). SSDD-Net: A Lightweight and Efficient Deep Learning Model for Steel Surface Defect Detection. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14434. Springer, Singapore. https://doi.org/10.1007/978-981-99-8549-4_20
Download citation
DOI: https://doi.org/10.1007/978-981-99-8549-4_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8548-7
Online ISBN: 978-981-99-8549-4
eBook Packages: Computer ScienceComputer Science (R0)