As critical infrastructure of the power transmission network, the structural safety of transmission towers directly influences the stable operation of the power grid. During long-term service, transmission tower bolts loosen gradually due to multi-factors coupling such as wind load, temperature difference effect, and material aging. A transmission tower bolt looseness intelligent detection model (CCSGS-YOLO) based on improved YOLOv5s is proposed in the paper. The coordinate convolution is used to replace the standard convolution layer in the backbone network to enhance the model's ability to obtain target location information. The convolutional block attention mechanism (CBAM) is introduced to strengthen the model's feature discrimination ability in complex backgrounds through a dual attention mechanism of channels and space. A Slim-Neck feature fusion structure is designed, which optimally combines cross-stage partial connections and depth-separable convolutions to maintain detection accuracy while reducing computational complexity. A combination of the Generalized Intersection over Union (GIoU) loss function and the Soft Non-Maximum Suppression (Soft-NMS) algorithm is used to optimize the positioning accuracy of object detection by considering the geometric characteristics of the overlap between the predicted box and the true box. The results show that CCSGS-YOLO achieves a precision of 91.7%, a recall of 89.4%, a mean average precision of 95.3%, and an F1-score of 90.0%, representing improvements of 1.6%, 3.0%, 1.4%, and 1% over the baseline model YOLOv5s, respectively. In terms of computational efficiency, the model achieves a detection speed of 74.8 FPS and reduces inference latency to 13.4 ms, which is an 11.6% improvement over the baseline model. The detection robustness of CCSGS-YOLO in different scenarios is verified through field experiments, which provides a new idea for intelligent inspection of transmission tower bolt looseness.