In the actual inspection process of overhead line insulators, drones need to process a large amount of different types of data in real time, which increases the difficulty of insulator defect detection. Therefore, a research on unmanned aerial vehicle intelligent detection of insulator defects on overhead lines based on multi-source data is proposed. Combining compressive sensing algorithm and wavelet transform algorithm, design pixel fusion rules for high-frequency and low-frequency regions of the image to be fused, and complete multi-source data fusion. By fusing different types of images, real-time and accurate comprehensive insulator information can be obtained. Add an MSRN network to the input of the traditional YOLOv5 network to optimize the image perception ability of the overhead line insulator defect detection model. Utilize coordinate attention mechanism to improve the model"s attention to insulator defect features and optimize defect recognition performance; Generate bounding boxes for defect identification using the CIoU Loss function to improve model detection accuracy. The experiment shows that the YOLOv5 overhead line insulator defect detection model established by the proposed method has good convergence ability and can accurately identify different types of overhead line insulator defects, which is helpful for the safe and stable operation and efficient maintenance of the power system, thereby helping to achieve the dual carbon and carbon peak goals.