Open Access

ARTICLE

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

Xin Ma^1,2, Jin Lei^3,4,*, Chenying Pei⁴, Chunming Wu⁴

1 Department of Aircraft Control and Information Engineering, Jilin University of Chemical Technology, Jilin, 132022, China
2 Micro Engineering and Micro Systems Laboratory, School of Mechanical and Aerospace Engineering, Jilin University, Changchun, 130025, China
3 School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, 710129, China
4 Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin, 132012, China

* Corresponding Author: Jin Lei. Email:

(This article belongs to the Special Issue: Advances in Object Detection: Methods and Applications)

Computers, Materials & Continua 2026, 86(2), 1-17. https://doi.org/10.32604/cmc.2025.069353

Received 20 June 2025; Accepted 25 September 2025; Issue published 09 December 2025

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.

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Keywords

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