A method for constructing a knowledge distillation model suitable for power line hardware detection

Abstract

The application provides a construction method of a knowledge distillation model suitable for power line fitting detection, comprising the following steps: inputting image data of power line fittings into a basic knowledge distillation model to extract multi-scale feature data; performing redundant compression processing and convolution kernel learning on the multi-scale feature data to obtain a light knowledge vector and fitting detection general knowledge; training the basic knowledge distillation model using the knowledge and teacher-student global knowledge prototypes, and performing differential distillation training on classification heads and regression heads based on objects and information effectiveness respectively in the process to obtain a preliminary knowledge distillation model; and combining a comprehensive loss function to perform end-to-end weighted optimization on a student model in the preliminary knowledge distillation model to obtain an optimized final knowledge distillation model. The application can solve the problems of high model complexity and insufficient adaptability to complex power fitting detection scenes in related art knowledge distillation-based detection methods.

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