• Proposes S3Det, a novel fast object detector for remote sensing images using ANN-to-SNN conversion.
• Introduces a fast sparse model for pulse sequence perception and channel self-decaying weighted normalization (CSWN) to reduce conversion error.
• Achieves accuracy comparable to the original ANN while consuming only 1.46 W, reducing power consumption by a factor of 122.
• Achieves 24.32% sparsity relative to the benchmark, enabling energy-efficient real-time remote sensing detection.