APPLE_YOLO: Apple Detection Method Based on Channel Pruning and Knowledge Distillation in Complicated Environments

This study proposes a lightweight apple detection method employing cascaded knowledge distillation (KD) to address the critical challenges of excessive parameters and high deployment costs in existing models. We introduce a Lightweight Feature Pyramid Network (LFPN) integrated with Lightweight Downsampling Convolutions (LDConv) to substantially reduce model complexity without compromising accuracy. A Lightweight Multi-channel Attention (LMCA) mechanism is incorporated between the backbone and neck networks to effectively suppress complex background interference in orchard environments. Furthermore, model size is compressed via Group_Slim channel pruning combined with a cascaded distillation strategy. Experimental results demonstrate that the proposed model achieves a 1% higher Average Precision (AP) than the baseline while maintaining extreme lightweight advantages (only 800 k parameters). Notably, the two-stage KD version achieves over 20 Frames Per Second (FPS) on Central Processing Unit (CPU) devices, confirming its practical deployability in real-world applications.