Abstract
Since surface defect detection is significant to ensure the utility, integrality, and security of productions, and it has become a key issue to control the quality of industrial products, which arouses interests of researchers. However, deploying deep convolutional neural networks (DCNNs) on embedded devices is very difficult due to limited storage space and computational resources. In this paper, an efficient lightweight convolutional neural network (CNN) model is designed for surface defect detection of industrial productions in the perspective of image processing via deep learning. By combining the inverse residual architecture with coordinate attention (CA) mechanism, a coordinate attention mobile (CAM) backbone network is constructed for feature extraction. Then, in order to solve the small object detection problem, the multi-scale strategy is developed by introducing the CA into the cross-layer information flow to improve the quality of feature extraction and augment the representation ability on multi-scale features. Hereafter, the multi-scale feature is integrated to design a novel bidirectional weighted feature pyramid network (BWFPN) to improve the model detection accuracy without increasing much computational burden. From the comparative experimental results on open source datasets, the effectiveness of the developed lightweight CNN is evaluated, and the detection accuracy attains on par with the state-of-the-art (SOTA) model with less parameters and calculation.
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Aiger D, Talbot H (2010) The phase only transform for unsupervised surface defect detection. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR). pp 295–302. https://doi.org/10.1109/CVPR.2010.5540198
Alex S, Dhanaraj KJ, Deepthi PP (2022) Private and energy-efficient decision tree-based disease detection for resource-constrained medical users in mobile healthcare network. IEEE Access 10:17098–17112. https://doi.org/10.1109/ACCESS.2022.3149771
Amir NIM, Dziyauddin RA, Mohamed N, et al (2022) Real-time threshold-based fall detection system using wearable iot. In: 2022 4th international conference on smart sensors and application (ICSSA). pp 173–178. https://doi.org/10.1109/ICSSA54161.2022.9870974
Bochkovskiy A, Wang CY, Liao H (2020) YOLOv4: optimal speed and accuracy of object detection. Preprint at http://arxiv.org/abs/2004.10934, https://doi.org/10.48550/arXiv.2004.10934
Cha YJ, Choi W, Büyüköztürk O (2017) Deep learning-based crack damage detection using convolutional neural networks. Comput-Aided Civil Infrastruct Eng 32(5):361–378. https://doi.org/10.1111/mice.12263
Cha YJ, Choi W, Suh G et al (2018) Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Comput-Aided Civil Infrastruct Eng 33(9):731–747. https://doi.org/10.1111/mice.12334
Chen Y, Ding Y, Zhao F et al (2021) Surface defect detection methods for industrial products: a review. Appl Sci 11(16):7657. https://doi.org/10.3390/app11167657
Chen G, Wang H, Chen K et al (2022) A survey of the four pillars for small object detection: multiscale representation, contextual information, super-resolution, and region proposal. IEEE Trans Syst Man Cybern Syst 52(2):936–953. https://doi.org/10.1109/TSMC.2020.3005231
Cross GR, Jain AK (1983) Markov random field texture models. IEEE Trans Pattern Anal Mach Intell 5(1):25–39. https://doi.org/10.1109/TPAMI.1983.4767341
Duan K, Bai S, Xie L, et al (2019) Centernet: Keypoint triplets for object detection. In: Proceedings of the IEEE/CVF international conference on computer vision (ICCV). pp 6568–6577. https://doi.org/10.1109/ICCV.2019.0066
Ghorai S, Mukherjee A, Gangadaran M et al (2013) Automatic defect detection on hot-rolled flat steel products. IEEE Trans Instrum Meas 62(3):612–621. https://doi.org/10.1109/TIM.2012.2218677
Han K, Wang Y, Tian Q, et al (2020) Ghostnet: More features from cheap operations. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR). pp 1577–1586. https://doi.org/10.1109/CVPR42600.2020.00165
Hasan AM, Meziane F, Jalab HA (2016) Performance of grey level statistic features versus gabor wavelet for screening mri brain tumors: a comparative study. In: 2016 6th international conference on information communication and management (ICICM). pp 136–140. https://doi.org/10.1109/INFOCOMAN.2016.7784230
He K, Zhang X, Ren S et al (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904–1916. https://doi.org/10.1109/TPAMI.2015.2389824
Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. In: 2021 IEEE/CVF conference on computer vision and pattern recognition (CVPR). pp 13708–13717. https://doi.org/10.1109/CVPR46437.2021.01350
Howard AG, Zhu M, Chen B, et al (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. Preprint at http://arxiv.org/abs/1704.04861, https://doi.org/10.48550/arXiv.1704.04861
Howard A, Sandler M, Chen B, et al (2019) Searching for mobilenetv3. In: 2019 IEEE/CVF international conference on computer vision (ICCV). pp 1314–1324. https://doi.org/10.1109/ICCV.2019.00140
Hu J, Shen L, Albanie S et al (2020) Squeeze-and-excitation networks. IEEE Trans Pattern Anal Mach Intell 42(8):2011–2023. https://doi.org/10.1109/TPAMI.2019.2913372
Huang PW, Lee CH (2009) Automatic classification for pathological prostate images based on fractal analysis. IEEE Trans Med Imaging 28(7):1037–1050. https://doi.org/10.1109/TMI.2009.2012704
Hui TW, Tang X, Loy CC (2018) Liteflownet: A lightweight convolutional neural network for optical flow estimation. In: 2018 IEEE/CVF conference on computer vision and pattern recognition. pp 8981–8989. https://doi.org/10.1109/CVPR.2018.00936
Kumar A, Pang G (2002) Defect detection in textured materials using optimized filters. IEEE Trans Syst Man Cybern B (Cybern) 32(5):553–570. https://doi.org/10.1109/TSMCB.2002.1033176
Li J, Su Z, Geng J et al (2018) Real-time detection of steel strip surface defects based on improved yolo detection network. IFAC-Papers OnLine 51(21):76–81. https://doi.org/10.1016/j.ifacol.2018.09.412
Li J, Pu Y, Tang J et al (2020) Deepatt: a hybrid category attention neural network for identifying functional effects of dna sequences. Brief Bioinform 22(3):159. https://doi.org/10.1093/bib/bbaa159
Li F, Li F, Xi Q (2021) Defectnet: toward fast and effective defect detection. IEEE Trans Instrum Meas 70:1–9. https://doi.org/10.1109/TIM.2021.3067221
Li F, Li Z, Liu J, et al (2022) Recognition method of two types of insulation joints based on wavelet transform and svm. In: 2022 global conference on robotics, artificial intelligence and information technology (GCRAIT). pp 736–741. https://doi.org/10.1109/GCRAIT55928.2022.00158
Lin TY, Dollár P, Girshick R, et al (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). pp 936–944. https://doi.org/10.1109/CVPR.2017.106
Lin WY, Lin CY, Chen GS, et al (2019) Steel surface defects detection based on deep learning. In: Proceedings of the international conference on applied human factors and ergonomics (AHFE). pp 141–149. https://doi.org/10.1007/978-3-319-94484-5_15
Lin TY, Goyal P, Girshick R et al (2020) Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell 42(2):318–327. https://doi.org/10.1109/TPAMI.2018.2858826
Liu L, Lao S, Fieguth PW et al (2016a) Median robust extended local binary pattern for texture classification. IEEE Trans Image Process 25(3):1368–1381. https://doi.org/10.1109/TIP.2016.2522378
Liu W, Anguelov D, Erhan D, et al (2016b) Ssd: single shot multibox detector. In: Proceedings of the IEEE European conference on computer vision (ECCV). pp 21–37. https://doi.org/10.1007/978-3-319-46448-0_2
Liu S, Qi L, Qin H, et al (2018) Path aggregation network for instance segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). pp 8759–8768. https://doi.org/10.1109/CVPR.2018.00913
Loshchilov I, Hutter F (2016) SGDR: stochastic gradient descent with restarts. Preprint at http://arxiv.org/abs/1608.03983, https://doi.org/10.48550/arXiv.1608.03983
Luo Q, Fang X, Liu L et al (2020) Automated visual defect detection for flat steel surface: a survey. IEEE Trans Instrum Meas 69(3):626–644. https://doi.org/10.1109/TIM.2019.2963555
Luo J, Yang Z, Li S et al (2021) Fpcb surface defect detection: a decoupled two-stage object detection framework. IEEE Trans Instrum Meas 70:1–11. https://doi.org/10.1109/TIM.2021.3092510
Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on feature distributions. Pattern Recogn 29(1):51–59. https://doi.org/10.1016/0031-3203(95)00067-4
Pan J, Yang A, Wang D et al (2022) Lightweight neural network for gas identification based on semiconductor sensor. IEEE Trans Instrum Meas 71:1–8. https://doi.org/10.1109/TIM.2021.3135503
Redmon J, Farhadi A (2018) YOLOv3: an incremental improvement. Preprint at http://arxiv.org/abs/1804.02767, https://doi.org/10.48550/arXiv.1804.02767
Ren S, He K, Girshick R et al (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031
Rezatofighi H, Tsoi N, Gwak J, et al (2019) Generalized intersection over union: A metric and a loss for bounding box regression. In: 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR). pp 658–666. https://doi.org/10.1109/CVPR.2019.00075
Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: 2018 IEEE/CVF conference on computer vision and pattern recognition. pp 4510–4520. https://doi.org/10.1109/CVPR.2018.00474
Shen J, Liu N, Xu C et al (2022) Finger vein recognition algorithm based on lightweight deep convolutional neural network. IEEE Trans Instrum Meas 71:1–13. https://doi.org/10.1109/TIM.2021.3132332
Song Y, Cai W, Zhou Y et al (2013) Feature-based image patch approximation for lung tissue classification. IEEE Trans Med Imaging 32(4):797–808
Song L, Lin W, Yang YG et al (2019) Weak micro-scratch detection based on deep convolutional neural network. IEEE Access 7:27547–27554. https://doi.org/10.1109/ACCESS.2019.2894863
Song Z, Zhang Y, Liu Y et al (2022) Msfyolo: feature fusion-based detection for small objects. IEEE Lat Am Trans 20(5):823–830. https://doi.org/10.1109/TLA.2022.9693567
Tan SC, Watada J, Ibrahim Z et al (2015) Evolutionary fuzzy artmap neural networks for classification of semiconductor defects. IEEE Trans Neural Netw Learn Syst 26(5):933–950. https://doi.org/10.1109/TNNLS.2014.2329097
Tan M, Pang R, Le QV (2020) EfficientDet: scalable and efficient object detection. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR). pp 10778–10787. https://doi.org/10.1109/CVPR42600.2020.01079
Wang L, Chang Y, Wang H et al (2017) An active contour model based on local fitted images for image segmentation. Inf Sci 418–419:61–73. https://doi.org/10.1016/j.ins.2017.06.042
Wang H, Liu C, Yu L, et al (2019) Research on target detection and recognition algorithm based on deep learning. In: Proceedings of the Chinese control conference (CCC). pp 8483–8487. https://doi.org/10.23919/ChiCC.2019.8865560
Wang Q, Wu B, Zhu P, et al (2020) Eca-net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR). pp 11531–11539. https://doi.org/10.1109/CVPR42600.2020.01155
Wang D, Zhang Z, Jiang Y et al (2021a) Dm3loc: multi-label mrna subcellular localization prediction and analysis based on multi-head self-attention mechanism. Nucleic Acids Res 49(8):e46. https://doi.org/10.1093/nar/gkab016
Wang H, Liu C, Zhao Z et al (2021b) Application of deep convolutional neural networks for discriminating benign, borderline, and malignant serous ovarian tumors from ultrasound images. Front Oncol 11:770683. https://doi.org/10.3389/fonc.2021.770683
Woo S, Park J, Lee JY et al (2018) Convolutional block attention module, vol 11211. Springer, Cham, pp 3–19. https://doi.org/10.1007/978-3-030-01234-2_1
Wu MS, Li CY (2021) Edge-based realtime image object detection for uav missions. In: 2021 30th wireless and optical communications conference (WOCC). pp 293–294. https://doi.org/10.1109/WOCC53213.2021.9602868
Wu T, Luo J, Fang J et al (2018) Unsupervised object-based change detection via a weibull mixture model-based binarization for high-resolution remote sensing images. IEEE Geosci Remote Sens Lett 15(1):63–67. https://doi.org/10.1109/LGRS.2017.2773118
Xie S, Shan S, Chen X et al (2010) Fusing local patterns of gabor magnitude and phase for face recognition. IEEE Trans Image Process 19(5):1349–1361. https://doi.org/10.1109/tip.2010.2041397
Xie L, Xiang X, Xu H et al (2021) Ffcnn: a deep neural network for surface defect detection of magnetic tile. IEEE Trans Ind Electron 68(4):3506–3516. https://doi.org/10.1109/TIE.2020.2982115
Yan Y, Kaneko S, Asano H (2020) Accumulated and aggregated shifting of intensity for defect detection on micro 3d textured surfaces. Pattern Recogn 98(107):057. https://doi.org/10.1016/j.patcog.2019.107057
Yang J, Li S, Wang Z et al (2020) Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24):5755. https://doi.org/10.3390/ma13245755
Zhao G, Yang H, Yu M (2020) Expression recognition method based on a lightweight convolutional neural network. IEEE Access 8:38528–38537. https://doi.org/10.1109/ACCESS.2020.2964752
Zheng G, Songtao L, Feng W, et al (2021a) YOLOx: exceeding yolo series in 2021. Preprint at http://arxiv.org/abs/2107.08430, https://doi.org/10.48550/arXiv.2107.08430
Zheng Z, Zhao J, Li Y (2021b) Research on detecting bearing-cover defects based on improved yolov3. IEEE Access 9:10304–10315. https://doi.org/10.1109/ACCESS.2021.3050484
Funding
This work was supported in part by the [National Natural Science Foundation of China] (Grant Numbers [62001359] and [61973330]), in part by [Foundation of Excellent Young-Backbone Teacher of Colleges and Universities in Henan Province] (Grant Number [2019GGJS182]), in part by [Key Scientific Research Project of Henan Colleges and Universities] (Grant Numbers [20A120005] and [21B120001]) and in part by [Postgraduate Cultivating Innovation and Quality Improvement Action Plan of Henan University] (Grant Number [SYLYC202219]).
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Zhang, D., Hao, X., Wang, D. et al. An efficient lightweight convolutional neural network for industrial surface defect detection. Artif Intell Rev 56, 10651–10677 (2023). https://doi.org/10.1007/s10462-023-10438-y
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DOI: https://doi.org/10.1007/s10462-023-10438-y