Abstract: This study addresses the task of Sound Event Detection (SED) in home environments, aiming to improve detection performance while achieving model lightweighting to meet the deployment needs of resource-constrained devices. A lightweight acoustic modeling framework named SKD-CRNN based on Self-Knowledge Distillation (SKD) is proposed. The method constructs a dual-branch Convolutional Recurrent Neural Network (CRNN) and introduces a self-distillation loss to guide the student branch to learn from the teacher branch, enabling knowledge transfer without an external teacher model. Experiments on the DCASE2023 Task4 dataset demonstrate that SKD-CRNN reduces the number of parameters to 80.7% of the baseline system while achieving consistent performance improvements in sound event detection across eight household sound event categories. Notably, the detection of non-instantaneous events such as electric shaver/toothbrush and dishes shows significant enhancement in onset and offset identification. Event-based, intersection-based, and segment-based F1 scores increase by 7.8%, 7.1%, and 6.2%, respectively, with PSDS1 and PSDS2 metrics improving by 2.5% and 3.2%. Experimental results demonstrate that SKD-CRNN improves SED detection capability while effectively reducing model complexity, making it suitable for edge deployment in home environments with limited computational resources.