All Title Author
Keywords Abstract

Publish in OALib Journal
ISSN: 2333-9721
APC: Only $99

ViewsDownloads

Relative Articles

More...

A CNN-Based Single-Stage Occlusion Real-Time Target Detection Method

DOI: 10.4236/jilsa.2024.161001, PP. 1-11

Keywords: Real-Time Mask Target, CNN (Convolutional Neural Network), Single-Stage Detection, Multi-Scale Feature Perception

Full-Text   Cite this paper   Add to My Lib

Abstract:

Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The method adopts the overall design of backbone network, detection network and algorithmic parameter optimisation method, completes the model training on the self-constructed occlusion target dataset, and adopts the multi-scale perception method for target detection. The HNM algorithm is used to screen positive and negative samples during the training process, and the NMS algorithm is used to post-process the prediction results during the detection process to improve the detection efficiency. After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection time of a single target on FDDB dataset is 39 ms, which can meet the need of real-time target detection. In addition, the project team has successfully deployed the method into substations and put it into use in many places in Beijing, which is important for achieving the anomaly of occlusion target detection.

References

[1]  Xia, Y.Q., Gao, R.Z. and Lin, M. (2020) Green Energy Complementary Based on Intelligent Power Plant Cloud Control System. Acta Automatica Sinica, 46, 1844-1868.
[2]  Chen, Y.J., Zhu, X.T. and Yu, Y.R. (2022) Empirical Analysis of Lightning Network: Topology, Evolution, and Fees. Ruan Jian Xue Bao/Journal of Software, 33, 3858-3873.
[3]  Zheng, H.B., Sun, Y.H., Liu, X.H., et al. (2021) Infrared Image Detection of Substation Insulators Using an Improved Fusion Single Shot Multibox Detector. IEEE Transactions on Power Delivery, 36, 3351-3359.
https://doi.org/10.1109/TPWRD.2020.3038880
[4]  Ala, G., Favuzza, S. and Mitolo, M., Musca, R. and Zizzo, G. (2022) Forensic Analysis of Fire in a Substation of a Commercial Center. IEEE Transactions on Industry Applications, 56, 3218-3223.
https://doi.org/10.1109/TIA.2020.2971675
[5]  Nassu, B.T., Marchesi, B., Wagner, R., et al. (2022) A Computer Vision System for Monitoring Disconnect Switches Distribution Substations. IEEE Transactions on Power Delivery, 37, 833-841.
https://doi.org/10.1109/TPWRD.2021.3071971
[6]  Balouji, E., Bäckström, K., McKelvey, T. and Salor, Ö. (2020) Deep-Learning-Based Harmonics and Interharmonics Predetection Designed for Compensating Significantly Time-Varying EAF Currents. IEEE Transactions on Industry Applications, 56, 3250-3260.
https://doi.org/10.1109/TIA.2020.2976722
[7]  Guan, X., Gao, W., Peng, H., Shu, N. and Gao, D.W. (2022) Image-Based Incipient Fault Classification of Electrical Substation Equipment by Transfer Learning of Deep Convolutional Neural Network. IEEE Canadian Journal of Electrical and Computer Engineering, 45, 1-8.
https://doi.org/10.1109/ICJECE.2021.3109293
[8]  Zheng, H.B., Cui, Y.H., Yang, W.Q., et al. (2022) An Infrared Image Detection Method of Substation Equipment Combining Iresgroup Structure and CenterNet. IEEE Transactions on Power Delivery, 37, 4757-4765.
https://doi.org/10.1109/TPWRD.2022.3158818
[9]  Ou, J.H., Wang, J.G., Xue, J., Zhou, X., et al. (2023) Infrared Image Target Detection of Substation Electrical Equipment Using an Improved Faster R-CNN. IEEE Transactions on Power Delivery, 38, 387-396.
https://doi.org/10.1109/TPWRD.2022.3191694
[10]  Han, S., Yang, F., Jiang, H., et al. (2021) A Smart Thermography Camera and Application in the Diagnosis of Electrical Equipment. IEEE Transactions on Instrumentation and Measurement, 70, 1-8.
https://doi.org/10.1109/TIM.2021.3094235
[11]  Li, J., Xu, Y., Nie, K., et al. (2023) PEDNet: A Lightweight Detection Network of Power Equipment in Infrared Image Based on YOLOv4-Tiny. IEEE Transactions on Instrumentation and Measurement, 72, 1-12.
https://doi.org/10.1109/TIM.2023.3235416
[12]  Zhou, N., Luo, L.E., Sheng, G.H. and Jiang, X.C. (2019) High Accuracy Insulation Fault Diagnosis Method of Power Equipment Based on Power Maximum Likelihood Estimation. IEEE Transactions on Power Delivery, 34, 1291-1299.
https://doi.org/10.1109/TPWRD.2018.2882230
[13]  Fan, Z., Shi, L., Xi, C., et al. (2022) Real Time Power Equipment Meter Recognition Based on Deep Learning. IEEE Transactions on Instrumentation and Measurement, 71, 1-15.
https://doi.org/10.1109/TIM.2022.3191709
[14]  Lin, T.-Y., Goyal, P., Girshick, R., He, K. and Dollár, P. (2017) Focal Loss for Dense Object Detection. 2017 IEEE International Conference on Computer Vision (ICCV), Venice, 22-29 October 2017, 2999-3007.
https://doi.org/10.1109/ICCV.2017.324
[15]  Yang, S., Luo, P., Loy, C.-C. and Tang, X. (2016) From Facial Parts Responses to Target Detection: A Deep Learning Approach. 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 7-13 December 2015, 3676-3684.
https://doi.org/10.1109/ICCV.2015.419
[16]  Kayhan, O.S. and van Gemert, J.C. (2020) On Translation Invariance in CNNs: Convolutional Layers Can Exploit Absolute Spatial Location. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, 13-19 June 2020, 14262-14273.
https://doi.org/10.1109/CVPR42600.2020.01428
[17]  Bai, Z.Q., Cui, Z.P., Rahim, J.A., Liu, X. and Tan, P. (2020) Deep Facial Non-Rigid Multi-View Stereo. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, 13-19 June 2020, 5850-5860.
https://doi.org/10.1109/CVPR42600.2020.00589
[18]  Hang, R., Liu, Q., Hong, D. and Ghamisi, P. (2019) Cascaded Recurrent Neural Networks for Hyperspectral Image Classification. IEEE Transactions on Geoscience & Remote Sensing, 57, 5384-5394.
https://doi.org/10.1109/TGRS.2019.2899129
[19]  Mou, L.C., Lu, X.Q., Li, X.L. and Zhu, X.X. (2020) Nonlocal Graph Convolutional Networks for Hyperspectral Image Classification. IEEE Transactions on Geoence and Remote Sensing, 58, 8246-8257.
https://doi.org/10.1109/TGRS.2020.2973363

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133

WeChat 1538708413