LP-YOLO: A Lightweight Object Detection Network Regarding Insect Pests for Mobile Terminal Devices Based on Improved YOLOv8

Yue Yu, Qi Zhou, Hao Wang, Ke Lv, Lijuan Zhang, Jian Li () and Dongming Li ()
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Yue Yu: School of Information Technology, Jilin Agricultural University, Changchun 130118, China
Qi Zhou: School of Computer Science, Xi’an Jiaotong University, Xi’an 710049, China
Hao Wang: School of Computer Science, Xi’an Jiaotong University, Xi’an 710049, China
Ke Lv: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Lijuan Zhang: College of Internet of Things Engineering, Wuxi University, Wuxi 214105, China
Jian Li: School of Information Technology, Jilin Agricultural University, Changchun 130118, China
Dongming Li: College of Internet of Things Engineering, Wuxi University, Wuxi 214105, China

Agriculture, 2024, vol. 14, issue 8, 1-24

Abstract: To enhance agricultural productivity through the accurate detection of pests under the constrained resources of mobile devices, we introduce LP-YOLO, a bespoke lightweight object detection framework optimized for mobile-based insect pest identification. Initially, we devise lightweight components, namely LP_Unit and LP_DownSample, to serve as direct substitutes for the majority of modules within YOLOv8. Subsequently, we develop an innovative attention mechanism, denoted as ECSA (Efficient Channel and Spatial Attention), which is integrated into the network to forge LP-YOLO(l). Moreover, assessing the trade-offs between parameter reduction and computational efficiency, considering both the backbone and head components of the network, we use structured pruning methods for the pruning process, culminating in the creation of LP-YOLO(s). Through a comprehensive series of evaluations on the IP102 dataset, the efficacy of LP-YOLO as a lightweight object detection model is validated. By incorporating fine-tuning techniques during training, LP-YOLO(s)n demonstrates a marginal mAP decrease of only 0.8% compared to YOLOv8n. However, it achieves a significant reduction in parameter count by 70.2% and a remarkable 40.7% increase in FPS, underscoring its efficiency and performance.

Keywords: object detection; pests; lightweight network; LP-YOLO; attention mechanism (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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