In this paper, an Image-Enhancement-EfficientNet-YOLOv3 (IEEY) network based on YOLOv3 is proposed to detect the hooking state of main and tail hooks, which is improved from two aspects. Firstly, considering the steelmaking plant's dark, dusty and smoky environment, the image enhancement method combined with steel slag convolutional neural network (SS-CNN) is studied and adopted for image pre-processing in the IEEY network. The SS-CNN network first understands the global content of the image, such as luminance, colour, hue, etc., and then predicts the hyperparameters of six image filters including: exposure, white balance, gamma, tone curve, contrast and sharpening. These filters are used to enhance the image details under dark light and smoke interference. Secondly, the Efficient Channel Attention Net (ECA-Net) is used to improve the Efficient Net target detection network for feature extraction. The EfficientNet-B0 model, which consists of multiple Mobile Inverted Bottleneck Convolutional (MBConv) blocks, is studied. The improved network is used to fuse the feature pyramid structure of YOLOv3. The experimental results show that our IEEY network achieves 99.1% mean average precision and 27.6 frames per second on the test set for six different states. This scheme has been proven effective in actual tests, with the results exhibiting a sufficiently tiny margin of error compared to the test results obtained from the test set.
YangJZhangLChenC, et al.A hierarchical deep convolutional neural network and gated recurrent unit framework for structural damage detection. Inf Sci2020; 540: 117–130.
2.
HuZQiWDingK, et al.An adaptive lighting indoor vSLAM with limited on-device resources. IEEE Internet Things J2024; 11(17): 28863–28875.
3.
YanPZhaoJHouR, et al.Clustered remote sensing target distribution detection aided by density-based spatial analysis. Int J Appl Earth Obs Geoinf2024; 132: 104019.
4.
XiaJYangZLiS, et al.Blind super-resolution via meta-learning and Markov chain Monte Carlo simulation. IEEE Trans Pattern Anal Mach Intell2024; 46(12): 8139–8156.
5.
HuangS-CLeT-HJawD-W. DSNet: joint semantic learning for object detection in inclement weather conditions. IEEE Trans Pattern Anal Mach Intell2021; 43: 2623–2633.
HuYHeHXuC, et al.Exposure: a white-box photo post-processing framework, ACM trans. Graph2018; 37: 1–17.
10.
ChengDChenLLvC, et al.Light-guided and cross-fusion U-Net for anti-illumination image super-resolution. IEEE Trans Circuits Syst Video Technol2022; 32: 8436–8449.
11.
LiuWRenGYuR, et al.Image-adaptive YOLO for object detection in adverse weather conditions. In: Proc AAAI Conf Artif Intell, 2022, pp.1792–1800.https://arxiv.org/abs/2112.08088.
12.
ZhaoZ-QZhengPXuS-T,et al.Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst2019; 30: 3212–3232.. 2876865.
RenSHeKGirshickR,et al.Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. In: Adv Neural Inf Process Syst.. Curran Associates, Inc., 2015. https://arxiv.org/abs/1506.01497 (accessed 23 November 2022).
15.
HeKZhangXRenS,et al.Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell2015; 37: 1904–1916.
YangJYangFZhouY, et al.A data-driven structural damage detection framework based on parallel convolutional neural network and bidirectional gated recurrent unit. Inf Sci2021; 566: 103–117.
21.
JiaYYuWChenG, et al.Nighttime road scene image enhancement based on cycle-consistent generative adversarial network. Sci Rep2024; 14: 14375.
22.
HuCDongBShaoH, et al.Toward purifying defect feature for multilabel sewer defect classification. IEEE Trans Instrum Meas2023; 72: 1–11.
BaghbanbashiMRajiMGhavamiB.Quantizing YOLOv7: a comprehensive study. In 2023 28th International Computer Conference, Computer Society of Iran (CSICC). Tehran: IEEE, 2023: pp.1–5.
33.
ReisDKupecJHongJ, et al.Real-time flying object detection with YOLOv8[EB/OL]. (2024-05-22)[2025-01-13]. https://arxiv.org/abs/2305.09972
34.
ChienCTJuRYChouKY, et al.YOLOv9 for fracture detection in pediatric wrist trauma X-ray images. Electron Lett2024; 60: 1–3.
35.
MeiJZhuW. BGF-YOLOv10: small object detection algorithm from unmanned aerial vehicle perspective based on improved YOLOv10. Sensors2024; 24: 6911.
36.
LiuWAnguelovDErhanD, et al.SSD: Single Shot MultiBox Detector. In European Conference on Computer Vision (ECCV). 2016.
