The real-time detection of power line fittings is very important to ensure the safe operation of the power grid.However

achieving both high precision and real-time performance in complex natural environments remains challenging.Depth detection models often have large parameters and high computational overhead

while the fittings have a wide variety and complex scenarios. Therefore

this paper proposes a lightweight detection method for hardware components based on knowledge distillation.The method introduces a lightweight integrated knowledge transfer module(LIKTM)and task adaptive region aware distillation(TARAD)

which significantly reduce model size and improve inference speed without sacrificing accuracy.LIKTM is composed of a lightweight encoder for shared knowledge(LESK)and a lightweight averaged prototype transfer(LAPT) strategy

which can compress feature redundancy

refine general knowledge

and alleviate the loss of distillation information. TARAD designed a regional adaptive strategy for the classification head and regression head

and selectively transferring soft labels and localizaiton cues of the teacher model in key candidate regions. Experimental results show that the proposed method achieves the highest mean average precision(MAP)under multiple groups of teacher-student combinations

which significantly improves the detection accuracy compared with the baseline.At the same time

the average number of model parameters is compressed to about 80%

and the inference speed is increased by nearly 4 times

achieving both high precision and high efficiency and lightweight.