An enhanced SSD with feature cross-reinforcement for small-object detection (original) (raw)

References

  1. Wei J, He J, Zhou Y, Chen K, Tang Z, Xiong Z (2020) Enhanced object detection with deep convolutional neural networks for Advanced driving assistance. IEEE Trans Intell Transp Syst 21(4):1572–1583
    Article Google Scholar
  2. Guo G, Wang H, Yan Y, Zheng J, Li B (2020) A fast face detection method via convolutional neural network. Neurocomputing 395:128–137
    Article Google Scholar
  3. Ferrag MA, Maglaras L, Moschoyiannis S, Janicke H (2020) Deep learning for cyber security intrusion detection: approaches, datasets, and comparative study. J Inform Secur Appl 50:1–19
    Google Scholar
  4. Qiu T, Wen C, Xie K, Wen F, Sheng G, Tang X (2019) Efficient medical image enhancement based on CNN-FBB model. IET Image Proc 13(10):1736–1744
    Article Google Scholar
  5. Retnamony J, Muniasamy S, Stanley B (2022) Enhanced global and local face feature extraction for effective recognition of facial emotions. Concurrency and Computation-Practice & Experience 34(5). https://doi.org/10.1002/cpe.6701
  6. Owczarek M (2020) The impact and importance of fabric image preprocessing for the new method of individual inter-thread pores detection. Autex Res J 20(3):250–262
    Article Google Scholar
  7. Zhu Y, Zhang F, Li L, Lin Y, Zhang Z, Shi L, Qin T (2021) Research on classification model of Panax notoginseng taproots based on machine Vision Feature Fusion. Sensors 21(23). https://doi.org/10.3390/s2123794
  8. Guo S, Liu F, Yuan X, Zou C, Chen L, Shen T (2021) HSPOG: an optimized target Recognition Method based on histogram of spatial pyramid oriented gradients. Tsinghua Sci Technol 26(4):475–483
    Article Google Scholar
  9. Perez-Benito F, Signol F, Perez-Cortes J, Pollan M, Perez-Gomez B, Salas-Trejo D, Llobet R (2019) Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts. Comput Methods Programs Biomed 177:123–132. https://doi.org/10.1016/j.cmpb.2019.05.022
    Article Google Scholar
  10. Safdari M, Moallem P, Satari M (2019) SIFT detector boosted by adaptive contrast threshold to improve matching robustness of Remote sensing panchromatic images. Ieee J Sel Top Appl Earth Observations Remote Sens 12(2):675–684. https://doi.org/10.1109/jstars.2019.2892360
    Article Google Scholar
  11. Doyle L, Mould D (2019) Augmenting photographs with textures using the Laplacian pyramid. Visual Comput 35(10):1489–1500
    Article Google Scholar
  12. Fu Z, Zhao Y, Xu Y, Xu L, Xu J (2020) Gradient structural similarity based gradient filtering for multi-modal image fusion. Inform Fusion 53:251–268. https://doi.org/10.1016/j.inffus.2019.06.025
    Article Google Scholar
  13. Xu J, Liu Z, Hou Y, Zhen X, Shao L, Cheng M (2021) Pixel-level non-local image smoothing with objective evaluation. IEEE Trans Multimedia 23:4065–4078
    Article Google Scholar
  14. Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaria J, Fadhel MA, Al-Amidie M, Farhan L (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8(1):1–74
    Article Google Scholar
  15. Zhou D (2018) Deep distributed convolutional neural networks: universality. Anal Appl 16(6):895–919
    Article MathSciNet MATH Google Scholar
  16. Mu R, Zeng X (2019) A review of deep learning research. KSII Trans Internet Inform Syst (TIIS) 13(4):1738–1764. https://doi.org/10.3837/tiis.2019.04.001
    Article Google Scholar
  17. Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84–90
    Article Google Scholar
  18. Wang J, Wang W, Gao W (2018) Multiscale Deep alternative neural network for large-scale video classification. IEEE Trans Multimedia 20(10):2578–2592. https://doi.org/10.1109/tmm.2018.2855081
    Article Google Scholar
  19. Wang D, Li Y, Ma L, Bai Z, Chan J (2019) Going deeper with densely connected convolutional neural networks for Multispectral Pansharpening. Remote Sens 11(22). https://doi.org/10.3390/rs11222608
  20. Ha V, Ren J, Xu X, Liao W, Zhao S, Ren J, Yan G (2020) Optimized highway deep learning network for fast single image super-resolution reconstruction. J Real-Time Image Proc 17(6):1961–1970. https://doi.org/10.1007/s11554-020-00973-0
    Article Google Scholar
  21. Lu Y, Dong L, Zhang T, Xu W (2020) A robust detection algorithm for Infrared Maritime Small and Dim targets. Sensors 20(4):1–19
    Article Google Scholar
  22. Li Y, Zhang D, Lee D (2019) IIRNet: a lightweight deep neural network using intensely inverted residuals for image recognition. Image Vis Comput 92:1–8
    Article Google Scholar
  23. Shelhamer E, Long J, Darrell T (2017) Fully Convolutional Networks for Semantic Segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640–651
    Article Google Scholar
  24. Rawat W, Wang Z (2017) Deep convolutional neural networks for image classification: a Comprehensive Review. Neural Comput 29(9):2352–2449
    Article MathSciNet MATH Google Scholar
  25. Zhu K, Wang R, Zhao Q, Cheng J, Tao D (2020) A cuboid CNN Model with an attention mechanism for Skeleton-Based action recognition. IEEE Trans Multimedia 22(11):2977–2989
    Article Google Scholar
  26. Omar W, Oh Y, Chung J, Lee I (2021) Aerial dataset integration for vehicle detection based on YOLOv4. Korean J Remote Sens 37(4):747–761. https://doi.org/10.7780/kjrs.2021.37.4.6
    Article Google Scholar
  27. Vinyals O, Toshev A, Bengio S, Erhan D (2017) Show and tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge. IEEE Trans Pattern Anal Mach Intell 39(4):652–663
    Article Google Scholar
  28. Xi R, Hou J, Lou W (2020) Potato bud detection with improved faster R-CNN. Trans Asabe 63(3):557–569. https://doi.org/10.13031/trans.13628
    Article Google Scholar
  29. Ren P, Wang L, Fang W, Song S, Djahel S (2020) A novel squeeze YOLO-based real-time people counting approach. Int J Bio-Inspired Comput 16(2):94–101. https://doi.org/10.1504/ijbic.2020.109674
    Article Google Scholar
  30. Biswas D, Su H, Wang C, Stevanovic A, Wang W (2019) An automatic traffic density estimation using single shot detection (SSD) and MobileNet-SSD. Phys Chem Earth 110:176–184. https://doi.org/10.1016/j.pce.2018.12.001
    Article Google Scholar
  31. Cao J, Song C, Song S, Peng S, Wang D, Shao Y, Xiao F (2020) Vehicle detection algorithm for Smart Car based on improved SSD model. Sensors 20(16):1–21
    Article Google Scholar
  32. Cheng Y, Liu W, Xing W (2021) Weighted feature fusion and attention mechanism for object detection. J Electron Imaging 30(2):1–12
    Article Google Scholar
  33. Zhou S, Qiu J (2021) Enhanced SSD with interactive multi-scale attention features for object detection. Multimedia Tools and Applications 80(8):11539–11556. https://doi.org/10.1007/s11042-020-10191-2
    Article MathSciNet Google Scholar
  34. Cai Z, Vasconcelos N (2021) Cascade R-CNN: high quality object detection and Instance Segmentation. IEEE Trans Pattern Anal Mach Intell 43(5):1483–1498
    Article Google Scholar
  35. Lin T, Goyal P, Girshick R, He K, Dollar P (2020) Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell 42(2):318–327
    Article Google Scholar
  36. Zhang K, Cui L, Yin Y (2020) A multivariate grey incidence model for different scale data based on spatial pyramid pooling. J Syst Eng Electron 31(4):770–779. https://doi.org/10.23919/jsee.2020.000052
    Article Google Scholar
  37. Li T, Yu Y, Huang C, Yang J, Zhong Y, Hao Y (2022) Method for predicting cutter remaining life based on multi-scale cyclic convolutional network. Int J Distrib Sens Netw 18(5). https://doi.org/10.1177/15501329221102077
  38. Chen S, Tan X, Wang B, Lu H, Hu X, Fu Y (2020) Reverse attention-based residual network for salient object detection. IEEE Trans Image Process 29:3763–3776
    Article MATH Google Scholar
  39. Hu J, Shen L, Albanie S, Sun G, Wu E (2020) Squeeze-and-excitation networks. IEEE Trans Pattern Anal Mach Intell 42(8):2011–2023
    Article Google Scholar
  40. Xue H, Sun M, Liang Y (2022) ECANet: explicit cyclic attention-based network for video saliency prediction. Neurocomputing 468:233–244
    Article Google Scholar
  41. Khan R, Khattak H, Wong W, AlSalman H, Mosleh M, Rahman S (2021) Intelligent Malaysian Sign Language Translation System Using Convolutional-Based Attention Module with Residual Network. Computational Intelligence and Neuroscience, 2021. doi:https://doi.org/10.1155/2021/9023010
  42. Lee H, Kwon H (2017) Going deeper with contextual CNN for Hyperspectral Image classification. IEEE Trans Image Process 26(10):4843–4855
    Article MathSciNet Google Scholar
  43. Poernomo A, Kang D (2018) Biased dropout and Crossmap Dropout: learning towards effective dropout regularization in convolutional neural network. Neural Netw 104:60–67. https://doi.org/10.1016/j.neunet.2018.03.016
    Article Google Scholar
  44. Abu Al-Haija Q (2022) Leveraging ShuffleNet transfer learning to enhance handwritten character recognition. Gene Expr Patterns 45. https://doi.org/10.1016/j.gep.2022.119263
  45. Wang J, Yu J, He Z (2022) DECA: a novel multi-scale efficient channel attention module for object detection in real-life fire images. Appl Intell 52(2):1362–1375. https://doi.org/10.1007/s10489-021-02496-y
    Article Google Scholar
  46. Elfwing S, Uchibe E, Doya K (2018) Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Netw 107:3–11
    Article Google Scholar
  47. Li S, Sultonov F, Tursunboev J, Park J, Yun S, Kang J (2022) Ghostformer: a GhostNet-Based two-stage transformer for small object detection. Sensors 22(18). https://doi.org/10.3390/s22186939
  48. Bai L, Zhao Y, Huang X (2018) A CNN Accelerator on FPGA using depthwise separable convolution. Ieee Trans Circuits Syst Ii-Express Briefs 65(10):1415–1419
    Google Scholar
  49. Wu K, Bai C, Wang D, Liu Z, Huang T, Zheng H (2021) Improved object detection algorithm of YOLOv3 Remote sensing image. Ieee Access 9:113889–113900. https://doi.org/10.1109/access.2021.3103522
    Article Google Scholar
  50. Yarotsky D (2017) Error bounds for approximations with deep ReLU networks. Neural Netw 94:103–114
    Article MATH Google Scholar
  51. Li M, Xu D, Zhang D, Zou J (2020) The seeding algorithms for spherical k-means clustering. J Global Optim 76(4):695–708. https://doi.org/10.1007/s10898-019-00779-w
    Article MathSciNet MATH Google Scholar
  52. Chang Y, Anagaw A, Chang L, Wang Y, Hsiao C, Lee W (2019) Ship detection based on YOLOv2 for SAR Imagery. Remote Sens 11(7). https://doi.org/10.3390/rs11070786
  53. Shen Z, Liu Z, Li J, Jiang Y, Chen Y, Xue X (2020) Object detection from scratch with Deep Supervision. IEEE Trans Pattern Anal Mach Intell 42(2):398–412. https://doi.org/10.1109/tpami.2019.2922181
    Article Google Scholar
  54. Ma F, Xu Y, Xu P (2021) Research on the Minimum size of received Signal Strength difference localization network. Int J Comput Intell Syst 14(1). https://doi.org/10.1007/s44196-021-00015-y
  55. Zhang Y, Zhou W, Wang Y, Xu L (2020) A real-time recognition method of static gesture based on DSSD. Multimedia Tools and Applications 79(25–26):17445–17461. https://doi.org/10.1007/s11042-020-08725-9
    Article Google Scholar
  56. Wang X, Wang J, Tang P, Liu W (2019) Weakly- and semi-supervised fast region-based CNN for object detection. J Comput Sci Technol 34(6):1269–1278. https://doi.org/10.1007/s11390-019-1975-z
    Article Google Scholar
  57. Zhang Y, Zhu S, Yu C, Zhao L (2022) Small-footprint keyword spotting based on gated Channel Transformation Sandglass residual neural network. Int J Pattern recognit Artif Intell 36(07). https://doi.org/10.1142/s0218001422580034
  58. Chen Y, Lai K, Liu D, Chen M (2022) TAGNet: triplet-attention graph networks for Hashtag recommendation. IEEE Trans Circuits Syst Video Technol 32(3):1148–1159. https://doi.org/10.1109/tcsvt.2021.3074599
    Article Google Scholar
  59. Zheng C, Zhang J, Hwang J, Huang B (2022) Double-branch Dehazing Network based on self-calibrated attentional convolution. Knowl Based Syst 240. https://doi.org/10.1016/j.knosys.2022.108148

Download references