@article{Ma2026, 
author = {Xin Ma and Jin Lei and Chenying Pei and Chunming Wu},
title = {APPLE_YOLO: Apple Detection Method Based on Channel Pruning and Knowledge Distillation in Complicated Environments},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {86},
number = {2},
pages = {1-17},
keywords = {LMCA, LFPN, LDConv, group_slim, distillation},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.069353},
doi = {10.32604/cmc.2025.069353},
abstract = {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.}
}