## 1 Introduction  
As the scale of power grid construction continues to expand, incidents such as engineering vehicles touching power lines, accidentally hitting tower poles, and illegal excavation of foundations frequently occur, posing potential threats to the safe operation of transmission lines. The \"Overhead Transmission Line Operation Management Specification\" indicates that regular inspections are essential for ensuring the stable and reliable operation of the power grid system. The use of drones for inspections is safe, reliable, cost-effective, and highly efficient, and has become a significant method for inspecting transmission lines, replacing manual inspections [1]. However, the vast amount of images generated during inspections not only imposes a tremendous workload on staff but also makes visual identification of inspection targets inefficient [2]. Therefore, the intelligent detection of engineering vehicles is of great significance for transmission-line inspection tasks and for promoting the development of digital power grids. Object detection algorithms are an important technology in the field of computer vision, used to identify specific targets in images or videos. With the continuous development of deep learning theories and image processing devices, there has been extensive research on detection algorithms in the context of transmission-line scenarios. [PERSON] et al. [3] proposed a defect detection algorithm for transmission-line bolts based on Faster R-CNN, utilizing SCNet-101 (sample consensus network-101) for feature extraction and improving the FPN (feature pyramid network) structure to enhance detection accuracy, though this also increased the model's parameter count. [PERSON] et al. [4], addressing the issue of low accuracy in insulator image defect detection algorithms in complex environments, re-clustered anchor box sizes, improved the BCE (binary cross-entropy loss) function, and increased the depth of convolutional layers in the SPP (spatial pyramid pooling) module, effectively enhancing algorithm accuracy. [PERSON] et al. [5] made improvements based on the YOLOv5 algorithm by introducing the SE_ECSP module to enhance feature weighting, adding a small target detection layer and improving the loss function to further enhance the accuracy for small targets. However, the model still experienced some missed detections. [PERSON] et al. [6], to address the issues of inconspicuous features and low detection accuracy for small targets, conducted cross-level channel feature fusion on feature maps of different scales and used the CA (coordinate attention) module to aggregate positional information, effectively improving the YOLO algorithm's small-target detection capability. [PERSON] et al. [7] utilized a transformer module on top of YOLOv5 to capture global information of the targets, achieving an mAP of 95.6%. However, small defect targets in shaded areas were still missed. Despite the significant achievements in object detection achieved in the existing research, challenges remain in complex environments. On one hand, existing models typically improve detection accuracy by deepening the network layers, which increases model complexity. On the other hand, due to the low resolution and insufficient feature information of small targets, the difficulty in multi-scale target detection mainly lies in the precise localization of small targets. To address the above issues, this paper improves the model structure and the non-maximum suppression (NMS) algorithm based on YOLOv5s, constructing a multi-scale target detection model. The main contributions include the following:  
1. To address the issue of large parameter size and high memory usage in the C3 module, a CGC3 module was constructed, including a lightweight bottleneck structure and CBAM attention. This reduces model complexity while improving detection accuracy through dimension interaction. 2.