## 1 Introduction
### Lightweight Object Detection Algorithm  
Lightweight object detection algorithms enhance model efficiency by reducing complexity, optimizing computational processes, and compressing parameters, while maintaining accuracy. To improve the detection efficiency for power equipment, the application of real-time lightweight algorithms in power systems is essential. Furthermore, efficiently running complex deep learning models on resource-constrained embedded systems and mobile devices has become a key focus of research.  
[PERSON] et al. [27] designed GhostNet, which enhances the model performance by extracting feature maps with reduced computational resources. [PERSON] et al. [28] developed MobileNets, which employs depthwise separable convolution to create lightweight models. MobileNetV2 [29] introduces an inverted residual structure, while MobileNetV3 [30] leverages AutoML techniques to improve the model performance, while reducing computational complexity. [PERSON] [31] proposed ShuffleNet, which optimizes the information flow between different channel groups using channel shuffle operations. ShuffleNetV2 [32] further refined the model design for improved compatibility with object hardware. [PERSON] et al. [33] developed EfficientNet through a neural architecture search, optimizing the width and depth ratios of the network to enhance efficiency. [PERSON] et al. [34] introduced RepVGG, which unifies different convolutional layer structures (such as standard and depthwise separable convolutions) during training, thereby achieving a higher inference efficiency by simplifying the network structure during inference.  
In recent years, with the introduction of the ViT [35] model, it has been demonstrated that transformers also hold great potential in the field of computer vision. Some researchers have focused on how to make ViT models more lightweight. LeViT [36] proposed a multi-stage transformer architecture that uses an attention mechanism for downsampling, significantly improving the computational efficiency. MobileViT [37], by introducing local convolution operations and a simplified transformer structure, retains the ViT's advantage in capturing global information, while maintaining the efficiency of a lightweight network. EdgeViTs [38] developed the transformer block and introduced a cost-effective bottleneck, achieving a better accuracy-latency balance. Additionally, DETRs [39] have garnered widespread attention in academia for eliminating various handcrafted components. Recently, lightweight object detection models based on DETRs have also emerged. Lite-DETR [40], specifically designed for efficiency, introduces an efficient encoder block, enhancing the model efficiency through optimizations to the architecture and computational requirements. RT-DETR [41], developed by Baidu, leverages ViT to efficiently process multi-scale features, achieving real-time performance, while maintaining a high accuracy.  
These models have achieved impressive performance with minimal FLOPs, ensuring high-performance object detection even under resource-constrained conditions, which is essential for practical applications in power equipment monitoring. To improve the real time inference capability of the model, this study employs more lightweight convolutional modules to replace the standard convolutions, thereby maintaining a low parameter count and computational complexity, while still delivering effective detection results.