An Improved YOLOP Lane-Line Detection Utilizing Feature Shift Aggregation for Intelligent Agricultural Machinery

Cundeng Wang, Xiyuan Chen (), Zhiyuan Jiao, Shuang Song and Zhen Ma
Additional contact information
Cundeng Wang: The Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology of Ministry of Education, Southeast University, Nanjing 210018, China
Xiyuan Chen: The Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology of Ministry of Education, Southeast University, Nanjing 210018, China
Zhiyuan Jiao: The Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology of Ministry of Education, Southeast University, Nanjing 210018, China
Shuang Song: The Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology of Ministry of Education, Southeast University, Nanjing 210018, China
Zhen Ma: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2025, vol. 15, issue 13, 1-21

Abstract: Agricultural factories utilize advanced facilities and technologies to cultivate crops in a controlled environment, enhancing operational yields and reducing reliance on natural resources. This is crucial for ensuring a stable supply of agricultural products year-round and plays a significant role in the transformation of agricultural modernization. Automated Guided Vehicles (AGVs) are commonly employed in agricultural factories due to their low ownership costs and high efficiency. However, small embedded devices on AGVs face significant challenges in managing multiple tasks while maintaining the required timeliness. Multi-task learning (MTL) is increasingly employed to enhance the efficiency and performance of detection models in joint detection tasks, such as lane-line detection, pedestrian detection, and obstacle detection. The YOLOP (You Only Look for Panoptic Driving Perception) model demonstrates strong performance in simultaneously addressing these tasks; detecting lane lines in changeable agricultural factory scenarios is yet a challenging task, limiting the subsequent accurate planning and control of AGVs. This paper proposes a feedback-based network for joint detection tasks (MTNet) that simultaneously detects pedestrians, automated guided vehicles (AGVs), and QR codes, while also performing lane-line segmentation. This approach addresses the challenge faced by using embedded devices mounted on AGVs, which are unable to run multiple models for different tasks in parallel due to limited computational resources. For lane-line detection tasks, we also propose an improved YOLOP lane-line detection algorithm based on feature shift aggregation. Homemade datasets were used for training and testing. Comparative experiments of our model with different models in the target-detection and lane-line detection tasks, respectively, show the progressiveness of our model. Surprisingly, we also obtained a significant improvement in the model’s processing speed. Furthermore, we conducted ablation experiments to assess the effectiveness of our improvements in lane-line detection, all of which outperformed the original detection model.

Keywords: agricultural factories; automated guided vehicles (AGVs); multi-task learning (MTL); changeable agricultural factory scenarios; MTNet; lane-line detection (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: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/13/1361/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/13/1361/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:13:p:1361-:d:1687223

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().