Transmission Tower Bolt Looseness Detection Based on Improved YOLOv5s

doi:10.12141/j.issn.1000-565X.250172

Abstract

Abstract:

As the critical infrastructure in power transmission networks, the structural stability of transmission towers directly impacts the safe and reliable operation of the power grid. During long-term service, bolts in the tower structure are prone to gradual loosening under the coupled effects of multiple factors such as wind loads, temperature variations, and material aging. This paper proposed an intelligent detection model for bolt looseness in transmission towers based on an improved YOLOv5s (named CCSGS-YOLO). The model incorporates several key enhancements: coordinate convolution replaces standard convolutional layers in the backbone network to strengthen the model’s ability to capture positional information of targets; a convolutional block attention module (CBAM) is introduced to strengthen the model’s feature discrimination capability in complex backgrounds through dual channel and spatial attention mechanisms; a slim-neck feature fusion architecture is constructed, leveraging an optimized combination of cross-stage partial connections and depthwise separable convolutions to reduce computational complexity while maintaining detection accuracy; a joint optimization strategy employing the Generalized Intersection over Union (GIoU) loss function and Soft Non-Maximum Suppression (Soft-NMS) improves localization accuracy by considering the geometric overlap characteristics between predicted and ground-truth bounding boxes. Experimental results demonstrate that CCSGS-YOLO achieves a precision of 91.7%, a recall of 89.4%, a mean average precision (mAP) of 95.3%, and an F₁ score of 90.0%. These metrics represent improvements of 1.6, 3.0, 1.4, and 1.0 percentage points, respectively, over the baseline YOLOv5s model. In terms of computational efficiency, the model achieves a detection speed of 74.8 frames per second (FPS), reducing the inference latency to 13.4 ms, which represents an 11.6% improvement compared to the YOLOv5s model. Furthermore, this paper validates the detection robustness of CCSGS-YOLO across various scenarios through field experiments, providing a novel approach for intelligent inspection of loose bolts on transmission towers.

Key words: transmission tower, bolt looseness, deep learning, target detection

CLC Number:

TP391.4

WANG Dehong, ZHANG Zixuan. Transmission Tower Bolt Looseness Detection Based on Improved YOLOv5s[J]. Journal of South China University of Technology(Natural Science Edition), 2026, 54(2): 25-37.

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CoordConv	CBAM	Slim-Neck	GIoU+Soft-NMS	P/%	R/%	mAP@0.5/%	参数量/10⁶	浮点运算次数/10⁹
×	×	×	×	90.1	86.4	93.9	7.02	16.0
√	×	×	×	90.5	87.5	94.3	7.02	16.0
×	√	×	×	92.5	89.0	95.0	7.03	16.1
×	×	√	×	92.3	87.7	94.6	4.23	13.1
×	×	×	√	91.3	88.7	94.7	7.02	16.0
√	√	√	√	91.7	89.4	95.3	4.24	13.2

模型	P/%	R/%	mAP@0.5/%	F₁/%	检测速度/（f‧s^-1）
YOLOv3-Tiny	85.7	79.3	87.3	82.0	65.6
YOLOv5s	90.1	86.4	93.9	89.0	67.0
YOLOv7-Tiny	89.4	87.8	94.0	89.0	69.4
YOLOv8n	90.7	88.0	94.3	88.0	72.2
CCSGS-YOLO	91.7	89.4	95.3	90.0	74.8