<p>This paper presents the design of a lightweight high-precision transmission line insulator intelligent classification and defect detection model in Multiple Scenarios. The model, named Triple Attention, SimAM and SlimNeck, SIoU, and BCKD Knowledge Distillation Optimisation-You Look Only Once (TSSSKD-YOLO), is designed for use in transmission line inspection. It addresses the issue of low detection accuracy and slow detection speed of target detection algorithms due to the complex application scenarios, difference in light intensity taken by unmanned aerial vehicles from multiple angles. In order to enhance the model's feature extraction capabilities, a triple attention mechanism and a parameter-free SimAM attention mechanism have been integrated into the backbone and neck networks respectively. Furthermore, the SlimNeck module is incorporated into the neck network with the objective of reducing parameter redundancy and enhancing detection speed. The initial CIoU loss function is substituted with the SIoU bounding box regression loss function, thus improving the precision of the algorithmic process. The enhanced model is subjected to BCKD knowledge distillation, thereby enhancing its accuracy without an increase in model parameters. The superiority of the TSSSKD-YOLO model is validated by experimental results obtained through ablation and distillation tests, which demonstrate an mAP50 of 95.9%, representing a 6.1% improvement over the baseline model and a 29.8% increase in detection speed. Moreover, the model's performance in complex lighting and background conditions has been shown to exhibit notable improvements in both detection accuracy and speed. These observations indicate that the model has the potential for deployment in intelligent detection systems for transmission line insulators.</p>