Journal of Agricultural Machinery
with the collaboration of Iranian Society of Mechanical Engineers (ISME)

YOLO11-DML: A Lightweight Object Detection Method for Cattle

Articles in Press

Document Type : Research Article- En

Authors

Zh. Wei 1
X. Zhang 1
X. Li 2
Zh. Yu 1
D. Wei 1
J. Ni 1

1 College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China

2 College of Agriculture, Northeast Agricultural University, Harbin 150030, China

https://doi.org/10.22067/jam.2026.96943.1454
Abstract
Reliable cow detection in barn environments remains challenging due to visual occlusion, overlapping animals, and cluttered backgrounds. To improve accuracy and real-time performance under such conditions, this study presents YOLO11-DML, an enhanced detection framework built upon YOLO11. To address the decline in recognition accuracy caused by cow overlaps, occlusions, and background interference, this paper has implemented optimisations in three aspects: feature extraction, attention mechanism, and lightweight detection head design: The C3K2-DIMB module is introduced into the Backbone and Neck, enhancing multi-scale feature modelling capabilities through dynamic convolution kernel weights; The mixed local channel attention (MLCA) hybrid local-channel attention mechanism is embedded at the end of the Backbone as an auxiliary feature enhancement module to refine hierarchical feature representations; A lightweight shared convolutional detection head (LSCDH) is designed for the detection head, effectively reducing parameter count and computational overhead while maintaining detection accuracy. Experiments conducted on the CBVD-5 and Dairy Cow datasets show that YOLO11-DML achieves a precision (P) of 92.57%, an F1-score of 89.07%, an mAP@0.5 of 93.11%, an mAP@0.5:0.95 of 59.37%, an inference speed of 105.79 FPS, a parameter count of 2.16M, 5.1 GFLOPs of floating-point operations, and a model size of only 4.5 MB. The research demonstrates that YOLO11-DML achieves high precision while maintaining a lightweight design and real-time performance, providing a feasible solution for multi-object dairy cow detection and behaviour monitoring in smart farms.

Keywords

C3K2-DIMB
Deep learning
Detection
LSCDH
YOLO11-DML
Subjects

Authors retain the copyright. This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

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Journal of Agricultural Machinery

Articles in Press, Accepted Manuscript
Available Online from 20 May 2026

  • Receive Date 12 December 2025
  • Revise Date 25 February 2026
  • Accept Date 28 February 2026
  • First Publish Date 20 May 2026

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