TY  - EJOU
AU  - Ma, Xin 
AU  - Lei, Jin 
AU  - Pei, Chenying 
AU  - Wu, Chunming 

TI  - APPLE_YOLO: Apple Detection Method Based on Channel Pruning and Knowledge Distillation in Complicated Environments
T2  - Computers, Materials \& Continua

PY  - 2026
VL  - 86
IS  - 2
SN  - 1546-2226

AB  - 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.
KW  - LMCA; LFPN; LDConv; group_slim; distillation

DO  - 10.32604/cmc.2025.069353