A lightweight transmission line fault detection method based on Leaner YOLOv7-Tiny is proposed to address the issues of parameter complexity and high computational load in existing fault detection algorithms for transmission lines, which hinder their deployment on devices like drones. The method aims to swiftly and accurately detect typical faults in transmission lines from aerial images. Leaner YOLOv7-Tiny is a streamlined version of YOLOv7-Tiny, which inherits the ELAN structure and replaces its backbone with depthwise separable convolutions to reduce model parameters. It integrates the SP attention mechanism to fuse multi-scale information and enhance small target recognition. An improved FCIoU Loss function is introduced to balance the contribution of high-quality and low-quality samples to the loss function, expediting model convergence and boosting detection accuracy. Experimental results show a 20% reduction in model size compared to the original YOLOv7-Tiny algorithm. Detection accuracy for small targets surpasses that of current mainstream lightweight object detection algorithms. This approach holds practical significance for transmission line fault detection. The paper introduces Leaner YOLOv7-Tiny, which reduces model parameters while boosting accuracy in detecting small targets. It maintains the ELAN structure of YOLOv7-Tiny and replaces the backbone with depth-separable convolutions from PP-LCNet. It introduces the SP attention mechanism for multi-scale feature extraction and an improved FCIoU Loss function to balance sample contributions. The model is evaluated on a dataset of transmission line faults, showing improved performance in detection accuracy and robustness. The results demonstrate that Leaner YOLOv7-Tiny achieves higher accuracy and is more efficient in detecting small targets compared to other lightweight models. The model is suitable for deployment on drones due to its lightweight design and high detection accuracy. The study concludes that Leaner YOLOv7-Tiny represents a significant advancement in lightweight object detection, particularly for challenging applications like transmission line fault detection via drones.A lightweight transmission line fault detection method based on Leaner YOLOv7-Tiny is proposed to address the issues of parameter complexity and high computational load in existing fault detection algorithms for transmission lines, which hinder their deployment on devices like drones. The method aims to swiftly and accurately detect typical faults in transmission lines from aerial images. Leaner YOLOv7-Tiny is a streamlined version of YOLOv7-Tiny, which inherits the ELAN structure and replaces its backbone with depthwise separable convolutions to reduce model parameters. It integrates the SP attention mechanism to fuse multi-scale information and enhance small target recognition. An improved FCIoU Loss function is introduced to balance the contribution of high-quality and low-quality samples to the loss function, expediting model convergence and boosting detection accuracy. Experimental results show a 20% reduction in model size compared to the original YOLOv7-Tiny algorithm. Detection accuracy for small targets surpasses that of current mainstream lightweight object detection algorithms. This approach holds practical significance for transmission line fault detection. The paper introduces Leaner YOLOv7-Tiny, which reduces model parameters while boosting accuracy in detecting small targets. It maintains the ELAN structure of YOLOv7-Tiny and replaces the backbone with depth-separable convolutions from PP-LCNet. It introduces the SP attention mechanism for multi-scale feature extraction and an improved FCIoU Loss function to balance sample contributions. The model is evaluated on a dataset of transmission line faults, showing improved performance in detection accuracy and robustness. The results demonstrate that Leaner YOLOv7-Tiny achieves higher accuracy and is more efficient in detecting small targets compared to other lightweight models. The model is suitable for deployment on drones due to its lightweight design and high detection accuracy. The study concludes that Leaner YOLOv7-Tiny represents a significant advancement in lightweight object detection, particularly for challenging applications like transmission line fault detection via drones.