AdaptoMixNet: detection of foreign objects on power transmission lines under severe weather conditions

Abstract

With the expansion of power transmission line scale, the surrounding environment is complex and susceptible to foreign objects, severely threatening its safe operation. The current algorithm lacks stability and real-time performance in small target detection and severe weather conditions. Therefore, this paper proposes a method for detecting foreign objects on power transmission lines under severe weather conditions based on AdaptoMixNet. First, an Adaptive Fusion Module (AFM) is introduced, which improves the model's accuracy and adaptability through multi-scale feature extraction, fine-grained information preservation, and enhancing context information. Second, an Adaptive Feature Pyramid Module (AEFPM) is proposed, which enhances the focus on local details while preserving global information, improving the stability and robustness of feature representation. Finally, the Neuron Expansion Recursion Adaptive Filter (CARAFE) is designed, which enhances feature extraction, adaptive filtering, and recursive mechanisms, improving detection accuracy, robustness, and computational efficiency. Experimental results show that the method of this paper exhibits excellent performance in the detection of foreign objects on power transmission lines under complex backgrounds and harsh weather conditions.