| Citation: |
DENG Changzheng, LIU Mingze, FU Tian, GONG Mengqing, LUO Bingjie. Infrared Image Recognition of Substation Equipment Based on Improved YOLOv7-Tiny Algorithm[J]. Infrared Technology , 2025, 47(1): 44-51.
|
To address the problem of low accuracy and slow recognition speed of infrared (IR) image target recognition of substation equipment in complex backgrounds, this study proposes an IR image recognition algorithm for substation equipment based on the improved YOLOv7-Tiny. First, the lightweight bottleneck structure GhostNetV2 bottleneck is introduced to replace a part of the CBS module and build a lightweight and efficient aggregation network known as a lightweight-efficient layer aggregation network. Simultaneously, a coordinate attention mechanism is embedded in the feature extraction stage to reduce the number of network parameters while strengthening the network's extraction of key features of the target and improving detection accuracy. The network coordinate loss function is replaced by SIoU_Loss to improve the anchor frame positioning accuracy and network convergence speed. The results show that the accuracy of the improved network is 96.28%, the detection rate is 26.42 frames/s, and the model size is reduced to 7.82 M. Compared with the original YOLOv7-Tiny algorithm, the detection rate is increased by 21.69%, the identification accuracy is improved, and the model size is reduced by 36.89%. These results meet the requirements of accurate real-time identification of substation equipment and lay a foundation for subsequent substation equipment fault diagnosis.
| [1] |
王晶, 姚邹静, 赵春晖. 基于红外图像时空特征的电力设备故障诊断[J]. 控制工程, 2021, 28(8): 1683-1690.
WANG Jing, YAO Zoujing, ZHAO Chunhui. A fault diagnosis method for power equipment based on spatiotemporal features of infrared images[J]. Control Engineering of China, 2021, 28(8): 1683-1690.
|
| [2] |
HAN S, YANG F, YANG G, et al. Electrical equipment identification in infrared images based on ROI-selected CNN method[J]. Electric Power Systems Research, 2020, 188: 106534. DOI: 10.1016/j.epsr.2020.106534
|
| [3] |
吴添权, 郭竞, 苟先太, 等. 基于改进高斯卷积核的变电站设备红外图像检测方法[J]. 红外技术, 2021, 43(3): 230-236. http://hwjs.nvir.cn/cn/article/id/478e34f8-3f81-4419-8619-af60c5fb27f0
WU Tianquan, GUO Jing, GOU Xiantai, et al. Method of detecting substation equipment in infrared images based on improved gaussian convolution kernel[J]. Infrared Technology, 2021, 43(3): 230-236. http://hwjs.nvir.cn/cn/article/id/478e34f8-3f81-4419-8619-af60c5fb27f0
|
| [4] |
DUAN K, BAI S, XIE L, et al. CenterNet: keypoint triplets for object detection[C]//IEEE International Conference on Computer Vision, 2019: 6569-6578.
|
| [5] |
邝楚文, 何望. 基于红外与可见光图像的目标检测算法[J]. 红外技术, 2022, 44(9): 912-919. http://hwjs.nvir.cn/cn/article/id/60c5ef39-1d9c-4918-842f-3d86b939f3a6
KUANG Chuwen, HE Wang. Object detection algorithm based on infrared and visible light images[J]. Infrared Technology, 2022, 44(9): 912-919. http://hwjs.nvir.cn/cn/article/id/60c5ef39-1d9c-4918-842f-3d86b939f3a6
|
| [6] |
王媛彬, 李媛媛, 段誉, 等. 基于轻量骨干网络和注意力结构的变电设备红外图像识别[J]. 电网技术, 2023, 47(10): 4358-4369.
WANG Yuanbin, LI Yuanyuan, DUAN Yu, et al. Infrared image recognition of power transformation equipment based on light backbone network and attention structure [J]. Power Grid Technology, 2023, 47(10): 4358-4369.
|
| [7] |
Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779-788.
|
| [8] |
HAN K, WANG Y, TIAN Q, et al. Ghostnet: More features from cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 1580-1589.
|
| [9] |
WANG Q, WU B, ZHU P, et al. ECA-Net: Efficient channel attention for deep convolutional neural networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 11534-11542.
|
| [10] |
王有元, 李后英, 梁玄鸿, 等. 基于红外图像的变电设备热缺陷自调整残差网络诊断模型[J]. 高电压技术, 2020, 46(9): 3000-3007.
WANG Youyuan, LI Houying, LIANG Xuanhong, et al. Thermal defect self-tuning residual network diagnosis model of substation equipment based on infrared image[J]. High Voltage Technology, 2020, 46(9): 3000-3007.
|
| [11] |
WANG C Y, Bochkovskiy A, LIAO H Y M. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 7464-7475.
|
| [12] |
TANG Y, HAN K, GUO J, et al. GhostNetv2: enhance cheap operation with long-range attention[J]. Advances in Neural Information Processing Systems, 2022, 35: 9969-9982.
|
| [13] |
HOU Q, ZHOU D, FENG J. Coordinate attention for efficient mobile network design[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 13713-13722.
|
| [14] |
LIN T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 2980-2988.
|
| [15] |
Gevorgyan Z. SIoU loss: More powerful learning for bounding box regression[J]. arXiv preprint arXiv: 2205.12740, 2022.
|
| [16] |
Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J/OL]. Advances in Neural Information Processing Systems, 2017, https://arxiv.org/abs/1706.03762.
|
| [1] | LIANG Xiuman, ZHAO Jiayang, YU Haifeng. Lightweight Underwater Target Detection Algorithm Based on YOLOv8[J]. Infrared Technology , 2024, 46(9): 1015-1024. |
| [2] | GAO Yongqi, YUAN Zhixiang. Improved YOLOv5-based Underwater Infrared Garbage Detection Algorithm[J]. Infrared Technology , 2024, 46(9): 994-1005. |
| [3] | WANG You, HAN Lixiang, FU Gui. Aerial Infrared Image Target Recognition Method Based on Improved YOLOv5s[J]. Infrared Technology , 2024, 46(7): 775-781, 801. |
| [4] | SHEN Lingyun, LANG Baihe, SONG Zhengxun, WEN Zhitao. Infrared Image Object Detection Method Based on DCS-YOLOv8 Model[J]. Infrared Technology , 2024, 46(5): 565-575. |
| [5] | GAO Mingming, LI Yuanzhou, MA Lei, NAN Jingchang, ZHOU Qianyi. YOLOv5-LR: A Rotating Object Detection Model for Remote Sensing Images[J]. Infrared Technology , 2024, 46(1): 43-51. |
| [6] | SHEN Lingyun, LANG Baihe, SONG Zhengxun, WEN Zhitao. Remote Sensing Image Target Detection Method Based on CSE-YOLOv5[J]. Infrared Technology , 2023, 45(11): 1187-1197. |
| [7] | LI Yankai, XU Yuanyuan, LIU Ziqi, CHEN Yuqing. Aerial Infrared Target Detection Based on Improved YOLO v3 Algorithm[J]. Infrared Technology , 2023, 45(4): 386-393. |
| [8] | SUN Jianbo, WANG Lijie, MA Jihui, GAO Wei. Photovoltaic Module Fault Detection Based on Improved YOLOv5s Algorithm[J]. Infrared Technology , 2023, 45(2): 202-208. |
| [9] | DAI Jian, ZHAO Xu, LI Lianpeng, LIU Wen, CHU Xinyue. Improved YOLOv5-based Infrared Dim-small Target Detection under Complex Background[J]. Infrared Technology , 2022, 44(5): 504-512. |
| [10] | LI Mukai, ZHANG Tao, CUI Wennan. Research of Infrared Small Pedestrian Target Detection Based on YOLOv3[J]. Infrared Technology , 2020, 42(2): 176-181. |
| 1. |
相敏月，涂振宇，孙逸飞，方强，马飞. 基于ECA和BIFPN的低照度环境下的行人目标检测算法. 智能计算机与应用. 2023(09): 189-193 .
|
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: