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Research Article

Night construction site detection based on ghost-YOLOX

Han Guijina Xi’an University of Posts & Telecommunications, Xi’an, Shaanxi, People's Republic of ChinaView further author information
,
Wang Ruixuana Xi’an University of Posts & Telecommunications, Xi’an, Shaanxi, People's Republic of ChinaCorrespondence[email protected]
View further author information
,
Xu WuYanb China Construction Eight Engineering Division, Xi’an, Shaanxi, People's Republic of ChinaView further author information
&
Li Juna Xi’an University of Posts & Telecommunications, Xi’an, Shaanxi, People's Republic of ChinaView further author information
Article: 2316015 | Received 17 Jul 2023, Accepted 02 Feb 2024, Published online: 14 Feb 2024

References

  • Bochkovskiy, A., Wang, C. Y., & Liao, H. (2020). YOLOv4: Optimal speed and accuracy of object detection. ArXiv abs/2004.10934 (2020): n. pag.
  • Chen, J. H., Deng, S., Wang, P., Huang, X., & Liu, Y. (2023a). Lightweight helmet detection algorithm using an improved YOLOv4. Sensors, 1256. https://doi.org/10.3390/s23031256
  • Chen, W. M., Li, C., & Guo, H. (2023b). A lightweight face-assisted object detection model for welding helmet use. Expert Systems with Applications, 221.
  • Cui, Z., Zhu, Y., Gu, L., Qi, G., Li, X. X., Gao, P., Zhang, Z. H., & Harada, T. (2022). RestoreDet: Degradation equivariant representation for object detection in low resolution images [J].
  • Fang, Y., Liao, B., Wang, X., Fang, J., Qi, J., Wu, R., Niu, J. W., & Liu, W. Y. (2021). You only look at one sequence: Rethinking transformer in vision through object detection [J].
  • Ge, Z., Liu, S., & Wang, F. (2019). YOLOX. Exceeding YOLO Series in 2021. IEEE/CVF International Conference on Computer Vision (ICCV), 9626–9635. https://doi.org/10.48550/arXiv.2107.08430.
  • Glenn, J. (2020). YOLOv5. https://github.com/ultralytics/yolov5.
  • Gong, A., Li, Z. H., & Liang, C. R. (2023). Real-time pedestrian detection algorithm for adjacent perception in multiple scenes at night [J]. Journal of Image and Graphics, 2693–2705.
  • Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., & Xu, C. (2020). GhostNet: More features from cheap operations. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA (pp. 1577–1586). https://doi.org/10.1109/CVPR42600.2020.00165.
  • Hou, Q., Zhou, D., & Feng, J. (2021). Coordinate attention for efficient mobile network design [J].
  • Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. https://doi.org/10.48550/arXiv.1704.04861.
  • Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. (2017). Squeeze-and-Excitation Networks[C]. IEEE.
  • Hussain, M. (2023). YOLO-v1 to YOLO-v8, the rise of YOLO and Its complementary nature toward digital manufacturing and industrial defect detection. Machines, 11(7), 677. https://doi.org/10.3390/machines11070677
  • Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. https://doi.org/10.48550/arXiv.1602.07360.
  • Li, C., Li, L., Jiang, H., Weng, K., Geng, Y. F., Li, L., Ke, Z., Li, Q. Y., Chen, M., Nie, W. Q., Li, Y. D., Zhang, B., Liang, L. Y., Xu, X. M., Chu, X. X., Wei, X. M., & Wei, X. L. (2022). YOLOv6: A single-stage object detection framework for industrial applications[J]. arXiv preprint arXiv:2209.02976.
  • Liang, M. F., & Li, L. (2021). Enhanced YOLOv3 fuzzy target detection based on adversarial neural network. Computer Applications and Software, 38(10), 221–228.
  • Liu, J. (2021). Multi-target detection method based on YOLOv4 convolutional neural network. Journal of Physics: Conference Series, 1883(1), 012075. https://doi.org/10.1088/1742-6596/1883/1/012075
  • Minsoo, P., Dai, Q. T., Jinyeong, B., & Seunghee, P. (2023). Small and overlapping worker detection at construction sites. Automation in Construction. ISSN 0926-5805.
  • Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: unified, real-time object detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015), Las Vegas (pp. 779–788). https://doi.org/10.1109/CVPR.2016.91.
  • Redmon, J., & Farhadi, A. (2017). Yolo9000: Better, faster, stronger[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (pp. 6517–6525). IEEE.
  • Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement [J]. arXiv e-prints.
  • Ren, S. Q., He, K. M., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137–1149. http://doi.org/10.1109/TPAMI.2016.2577031
  • Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018), 4510–4520. https://doi.org/10.48550/arXiv.1801.04381.
  • Shi, H. Q., Xie, H., Zhang, M. Y., Jiang, L., & Chen, R. (2023). A low-illumination pedestrian detection algorithm based on attention mechanism[J]. Internet of Things Technology, 13(2), 27–29.
  • Shivanthan, Y., Andy, S., Dyer, A. G., & Song, A. (2018). Saliency preservation in low-resolution grayscale images. In European Conference on Computer Vision (2018) (pp. 237–254).
  • Shu, Z. T., Zhang, Z. B., Song, Y. Z., Wu, M. M., & Yuan, X. B. (2023). Low-illumination image target detection based on improved YOLOv5[J]. Laser & Optoelectronics Progress, 60, 4–8. https://doi.org/10.3788/LOP212965.
  • Sunkara, R., & Luo, T. (2023). No more Strided convolutions or pooling: A new CNN building block for low-resolution images and small objects. Machine Learning and Knowledge Discovery in Databases (2022).
  • Tang, K., Chen, L., Zhang, Z. J., & Zhang, S. Y. (2020). Statistical analysis and countermeasures of production safety accidents in China's construction industry [J]. Construction Safety, 40–43.
  • Wang, C. Y., Bochkovskiy, A., & Liao, H. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[J]. arXiv e-prints.
  • Wang, J., Chen, Y., Dong, Z., & Gao, M. (2023a). Improved YOLOv5 network for real-time multi-scale traffic sign detection. Neural Computing and Applications, 35(10), 7853–7865. https://doi.org/10.1007/s00521-022-08077-5
  • Wang, Y. X., Wang, Z., Wu, B., & Yang, G. (2021). A review of safety helmet wear detection algorithms for smart construction sites [J]. Journal of Wuhan University of Technology, 56–62.
  • Wang, Z. J., Cai, Z., & Wu, Y. (2023b). An improved YOLOX approach for low-light and small object detection: PPE on tunnel construction sites. Journal of Computational Design and Engineering, 10(3), 1158–1175. https://doi.org/10.1093/jcde/qwad042
  • Woo, S., Park, J., Lee, J. Y., & Kweon, I. S. (2018). Cbam: Convolutional block attention module[J]. Springer.
  • Yang, J. G., Ren, L., & Sun, S. Q. (2023). A conveyor belt roller detection algorithm for coal transportation at night [J]. Mining Equipment, 56–59.
  • Yang, L. X., Zhang, R.-Y., Li, L., & Xie, X. (2021). Simam: A simple, parameter-free attention module for convolutional neural networks. International Conference on Machine Learning (2021).
  • Zhang, R., Gao, S. B., Zhao, X., & Hou, X. L. (2023). Night-time vehicle target detection algorithm for autonomous driving based on improved YOLOv5s[J/OL]. Electronic Measurement Technology, 1–9. http://kns.cnki.net/kcms/detail/11.2175.TN.20230523.1113.002.html.

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