Optimizing foreign fiber segmentation performance with DeepLab V3+ and GAN in industrial IoE environments^✩

Abstract

In industrial Internet of Everything (IoE) environments, the precise detection of tiny foreign fibers on the surface of bobbin yarns is crucial for ensuring the quality of textile products. However, detecting these fibers often exceeds the capabilities of both human vision and existing automation equipment. To address this challenge, this research proposes a novel foreign fiber segmentation method that integrates Generative Adversarial Networks (GANs) with an enhanced encoder-decoder architecture, significantly improving detection accuracy in industrial IoE scenarios. Specifically, we develop a dual-path attention encoding network that synergistically combines MobileNetV2’s computational efficiency with ContextNet’s multi-scale contextual awareness, thereby enhancing the extraction of contextual features for tiny foreign fibers. A hybrid channel-spatial attention mechanism is designed by parallel integration of channel-wise excitation and spatial attention mapping, which substantially strengthens the capture of discriminative features for tiny foreign fibers in high-resolution images. The decoding stage employs dense skip-connections to construct multi-scale feature propagation paths, optimizing detail preservation during upsampling processes. To tackle the severe class imbalance in fiber-background pixel distribution, this research introduces a Weighted Binary Cross-Entropy (WBCE) loss function with adaptive focal weighting. Experimental results demonstrate that the proposed DeepLab-DPA framework achieves 98.77% Accuracy, 85.93% MIoU, and balanced performance metrics (87.01% Precision, 86.84% Recall, 86.92% F1-Score), confirming its effectiveness for industrial fiber detection tasks.

Keywords

Industrial IoE / Data generation / Foreign fiber / Semantic segmentation / Attention mechanism

CRediT authorship contribution statement

Shuo Yang: Writing -- original draft, Methodology, Investigation, Conceptualization. Jingbin Li: Writing -- review & editing, Supervision, Resources, Funding acquisition. Yang Li: Writing -- review & editing, Supervision, Investigation. Jing Nie: Supervision, Investigation. Dian Guo: Data curation. Liqing Hu: Resources. Yugang Feng: Writing – original draft, Methodology, Investigation, Conceptualization. Liansheng Zhang: Investigation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported by the Science and Technology Program Project of the Seventh Division of Xinjiang Production and Construction Corps (No. QS2023002).

References

[1] P.—y. Chu, K.—f. Choi, Y.—c. Chuang, C.—w. Kan, Comparison of performance of fabrics made of torque—free and conventional ring spun yarn with different varieties of cotton fibres, Fiber Polym. 22 (7) (2021) 2036-2043.

[2] J. Nie, Y. Yuan, Y. Li, J. Li, A. Shankar, B. Abu—Salih, J.J. Rodrigues, Full—progress crop management and harvesting scheme with integrated space information: a case of jujube orchard, J. Ind. Inf. Integr. 42 (2024) 100699.

[3] J. Nie, Y. Wang, Y. Li, X. Chao, Sustainable computing in smart agriculture: survey and challenges, Turk. J. Agric. For. 46 (4) (2022) 550-566.

[4] K. Song, J. Nie, Y. Li, J. Li, P. Song, S. Ercisli, Regional soil water content monitoring based on time—frequency spectrogram of low—frequency swept acoustic signal, Geoderma 441 (2024) 116765.

[5] Y. Li, S. Ercisli, Data—efficient crop pest recognition based on knn distance entropy, Sustain. Comput. Inf. Syst. 38 (2023) 100860.

[6] Weijian Tao, Zufan Zhang, Xi Liu, Maobin Yang, A fusion deep learning framework based on breast cancer grade prediction, Digit. Commun. Netw. 10 (6) (2024) 1782-1789.

[7] Yingchun Wang, Jingyi Wang, Weizhan Zhang, Yufeng Zhan, Song Guo, Qinghua Zheng, Xuanyu Wang, A survey on deploying mobile deep learning applications: a systemic and technical perspective, Digit. Commun. Netw. 8 (1) (2022) 1-17.

[8] Y. Li, J. Chen, J. Nie, J. Li, S. Ercisli, Low—carbon jujube moisture content detection based on spectral selection and reconstruction, IEEE Internet Things J. 11 (24) (2024) 38953-38964.

[9] J. Wang, Z. Zhang, L. Luo, H. Wei, W. Wang, M. Chen, S. Luo, Dualseg: fusing transformer and cnn structure for image segmentation in complex vineyard environment, Comput. Electron. Agric. 206 (2023) 107682.

[10] J. Wu, C. Wu, Y. Lin, T. Yoshinaga, L. Zhong, X. Chen, Y. Ji, Semantic segmentation—based semantic communication system for image transmission, Digit. Commun. Netw. 10 (3) (2024) 519-527.

[11] J. Nie, J. Jiang, Y. Li, H. Wang, S. Ercisli, L. Lv, Data and domain knowledge dual—driven artificial intelligence: survey, applications, and challenges, Expert Syst. 42 (1) (2023) 13425.

[12] X. Chao, Y. Li, Semisupervised few—shot remote sensing image classification based on knn distance entropy, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 15 (2022) 8798-8805.

[13] H. Kabir, M.—L. Tham, Y.C. Chang, Internet of robotic things for mobile robots: concepts, technologies, challenges, applications, and future directions, Digit. Commun. Netw. 9 (6) (2023) 1265-1290.

[14] H.F. Le, L.J. Zhang, Y.X. Liu, Surface defect detection of industrial parts based on yolov5, IEEE Access 10 (2022) 130784-130794.

[15] Y. Fang, Q. Wu, S. Li, J. Guan, Y. Cui, Enhanced yolov5s—based algorithm for industrial part detection, Sensors 24 (4) (2024) 1183.

[16] Z.—K. Zhang, M.—L. Zhou, R. Shao, M. Li, G. Li, A defect detection model for industrial products based on attention and knowledge distillation, Comput. Intell. Neurosci. 2022 (2022) 6174255.

[17] C. Zhang, T. Li, W. Zhang, The detection of impurity content in machine—picked seed cotton based on image processing and improved yolo v4, Agronomy Basel 12 (1) (2022) 66.

[18] W. Zi—kai, W. Jie, Z. Ruo—yu, Z. Meng—yun, Classification of foreign matter in cotton using line scan hyperspectral transmittance imaging, Spectrosc. Spectr. Anal. 43 (10) (2023) 3230-3238.

[19] R. Wang, Z.—F. Zhang, B. Yang, H.—Q. Xi, Y.—S. Zhai, R.—L. Zhang, L.—J. Geng, Z.—Y. Chen, K. Yang, Detection and classification of cotton foreign fibers based on polarization imaging and improved yolov5, Sensors 23 (9) (2023) 4415.

[20] W. Ren, Y. Du, Development and application of an interval—valued Pythagorean fuzzy set—based grade evaluation method to foreign fiber content, J. Nat. Fibers 20 (2) (2023) 2219906.

[21] S. Hu, Z. Wang, S. Zhang, X. Zhao, Foreign fibers detection using improved otsu—based maximum entropy algorithm in spinning process, in: 2022 6th International Conference on Robotics and Automation Sciences (ICRAS 2022), IEEE, 2022, pp. 206-210.

[22] S. Yang, J. Li, Y. Li, J. Nie, S. Ercisli, M.A. Khan, Imbalanced segmentation for abnormal cotton fiber based on gan and multiscale residual u—net, Alex. Eng. J. 106 (2024) 25-41.

[23] B. Liu, J. Hu, X. Bi, W. Li, X. Gao, Pgnet: positioning guidance network for semantic segmentation of very—high—resolution remote sensing images, Remote Sens. 14 (17) (2022) 4219.

[24] Y. Chong, X. Chen, S. Pan, Context union edge network for semantic segmentation of small—scale objects in very high resolution remote sensing images, IEEE Geosci. Remote Sens. Lett. 19 (2022) 6000305.

[25] Q. Zheng, L. Xu, F. Wang, Y. Xu, C. Lin, G. Zhang, Hilbertscnet: self—attention networks for small target segmentation of aerial drone images, Appl. Soft Comput. 150 (2024) 111035.

[26] P. Fan, Y. Diao, F. Li, W. Zhao, Z. Chen, Srsegnet: super—resolution—assisted small targets polyp segmentation network with combined high and low resolution, J. King Saud Univ, Comput. Inf. Sci. 36 (3) (2024) 101981.

[27] Q. Chong, M. Ni, J. Huang, Z. Liang, J. Wang, Z. Li, J. Xu, Pos—danet: a dual—branch awareness network for small object segmentation within high—resolution remote sensing images, Eng. Appl. Artif. Intell. 133 (A) (2024) 107960.

[28] Z. Liu, D. Yang, S. Wang, H. Su, Adaptive multi—channel Bayesian graph attention network for iot transaction security, Digit. Commun. Netw. 10 (3) (2024) 631-644.

[29] J. Ye, S. Xue, A. Jiang, Attention—based spatio—temporal graph convolutional network considering external factors for multi—step traffic flow prediction, Digit. Commun. Netw. 8 (3) (2022) 343-350.

[30] Q. Zhang, X. Zhang, H. Hu, C. Li, Y. Lin, R. Ma, Sports match prediction model for training and exercise using attention—based lstm network, Digit. Commun. Netw. 8 (4) (2022) 508-515.

[31] P. Wei, B. Wang, X. Dai, L. Li, F. He, A novel intrusion detection model for the can bus packet of in—vehicle network based on attention mechanism and autoencoder, Digit. Commun. Netw. 9 (1) (2023) 14-21.

[32] A. Bhujel, N.—E. Kim, E. Arulmozhi, J.K. Basak, H.—T. Kim, A lightweight attention—based convolutional neural networks for tomato leaf disease classification, Agriculture Basel 12 (2) (2022) 228.

[33] H. Gong, T. Mu, Q. Li, H. Dai, C. Li, Z. He, W. Wang, F. Han, A. Tuniyazi, H. Li, X. Lang, Z. Li, B. Wang, Swin—transformer—enabled yolov5 with attention mechanism for small object detection on satellite images, Remote Sens. 14 (12) (2022) 2861.

[34] J. Hang, Y. Wu, Y. Li, T. Lai, J. Zhang, Y. Li, A deep learning semantic segmentation network with attention mechanism for concrete crack detection, Struct. Health Monit. Int. J. 22 (5) (2023) 3006-3026.

[35] Z. Hao, Z. Wang, D. Bai, B. Tao, X. Tong, B. Chen, Intelligent detection of steel defects based on improved split attention networks, Front. Bioeng. Biotechnol. 9 (2022) 810876.

[36] Y. Li, G. Lu, J. Li, Z. Zhang, D. Zhang, Facial expression recognition in the wild using multi—level features and attention mechanisms, IEEE Trans. Affect. Comput. 14 (1) (2023) 451-462.

[37] S. Zhang, Z. Liu, Y. Chen, Y. Jin, G. Bai, Selective kernel convolution deep residual network based on channel—spatial attention mechanism and feature fusion for mechanical fault diagnosis, ISA Trans. 133 (2023) 369-383.

[38] L.—C. Chen, Y. Zhu, G. Papandreou, F. Schroff, H. Adam, Encoder—decoder with atrous separable convolution for semantic image segmentation, in: 15th European Conference on Computer Vision (ECCV), 2018, pp. 833-851.

[39] H. Zhao, J. Shi, X. Qi, X. Wang, J. Jia, Pyramid scene parsing network, in: 30th IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2017, pp. 6230-6239.

[40] V. Badrinarayanan, A. Kendall, R. Cipolla, Segnet: a deep convolutional encoder—decoder architecture for image segmentation, IEEE Trans. Pattern Anal. Mach. Intell. 39 (12) (2017) 2481-2495.

[41] L.—C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A.L. Yuille, Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, IEEE Trans. Pattern Anal. Mach. Intell. 40 (4) (2018) 834-848.