A Dual-Channel Metal Detection Method Based on RIME Optimization Algorithm

This paper proposes a novel dual-channel metal detection method that integrates the Frost Optimization Algorithm with a BiLSTM-Attention framework to enhance detection accuracy, material discrimination, and anti-interference performance in complex environments. Utilizing the RIME algorithm for multi-frequency scanning, the proposed method achieves a detection accuracy of 98.33% on a self-constructed dataset with 10 metallic types, outperforming classical models such as CNNs and LSTMs. Experimental results, supported by a confusion matrix and ablation studies, validate the model's robustness and superiority. This approach offers significant potential for industrial quality control, security screening, and archaeological exploration.