YOLOv7-tiny Transmission Line Foreign Object Detection Algorithm Based on Channel Pruning

被引：0

作者：

Sun, Yang ^{[1
]}

Li, Jia ^{[1
]}

机构：

[1] School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin

来源：

Computer Engineering and Applications | 2024年 / 60卷 / 14期

关键词：

channel pruning; foreign object detection; transmission lines; YOLOv7-tiny algorithm;

D O I：

10.3778/j.issn.1002-8331.2311-0160

中图分类号：

学科分类号：

摘要：

In response to the problem of poor accuracy and the large model size in transmission line foreign object detection, an improved YOLOv7-tiny algorithm based on channel pruning has been proposed. Firstly, the ReXNet network is used to replace the backbone network of YOLOv7-tiny to address the feature bottleneck issue in the original network. Secondly, diversified branch blocks are introduced to enhance the network’s feature fusion capability. Finally, through layer-adaptive magnitude-based pruning (LAMP), a pruning approach is employed to trade off some accuracy for a reduction in model size and computational load, preparing it for deployment on embedded devices. Experimental results indicate that the final improved model achieves a 3 percentage points increase in accuracy compared to the YOLOv7-tiny model, a 119.4% increase in FPS, and compresses the model size to 14% of the original size. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.

引用

页码：319 / 328

页数：9

共 22 条

[1]

HUANG X B, LI H B, ZHU Y C., Short-term ice accretion fore casting model for transmission lines with modified time-series analysis by fireworks algorithm, IET Generation, Transmission & Distribution, 12, 5, pp. 1074-1080, (2018)

[2]

CHEN J, ZHU S K, SUN Q., Bird’s nest intelligent detection on transmission lines for unmanned aerial vehicles front-end lightweight application, Journal of Fuzhou University(Natural Science Edition), 51, 4, pp. 539-546, (2023)

[3]

WANG Y B, SHANG J L, ZHANG Y, Et al., Real-time transmission wire defect detection method based on improved YOLOv7, Southern Power System Technology, 17, 12, pp. 127-134, (2023)

[4]

YANG F, DING Z T, XING M M, Et al., Review of object detection algorithm improvement in deep learning, Computer Engineering and Applications, 59, 11, pp. 1-15, (2023)

[5]

SHAO G W, LIU Z, FU J, Et al., Research progress in unmanned aerial vehicle inspection technology on overhead transmission lines, High Voltage Engineering, 46, 1, pp. 14-22, (2020)

[6]

GIRSHICK R, DONAHUE J, DARRELL T, Et al., Rich feature hierarchies for accurate object detection and semantic segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580-587, (2014)

[7]

REDMON J, DIVVALA S, GIRSHICK R, Et al., You only look once: unified, real-time object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788, (2016)

[8]

LIU W, ANGUELOV D, ERHAN D, Et al., SSD: single shot multibox detector, Proceedings of the European Conference on Computer Vision, pp. 21-37, (2016)

[9]

LIN T Y, GOYAL P, GIRSHICK R, Et al., Focal loss for dense object detection, Proceedings of the IEEE International Conference on Computer Vision, pp. 2980-2988, (2017)

[10]

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, (2022)

← 1 2 3 →