Aug 2026· Animals· Vol 16, pp. 2520· 0 citations· 37 references
Medicine
TL;DR
This study provides an efficient detection approach for dairy cow monitoring in complex farming environments using dynamic multi-scale feature extraction and parameter-free attention to enhance discriminative features under occlusion and low-contrast conditions.
Abstract
Simple Summary Accurate dairy cow detection is important for automatic barn monitoring, but it remains challenging because cattle often appear at different scales, overlap with each other, and are captured under uneven illumination. The model addresses three practical challenges: dynamic multi-scale feature extraction is used to represent cattle with different body sizes and poses, parameter-free attention is introduced to enhance discriminative features under occlusion and low-contrast conditions, and a lightweight shared detection head is designed to reduce model complexity. Experimental results show that DMFRNet improves localization accuracy while maintaining a compact model size. This study provides an efficient detection approach for dairy cow monitoring in complex farming environments.
Ground cattle detection remains a challenging task due to dense object distribution, significant scale variation, interference from complicated backgrounds and challenges in identifying tiny objects. This research suggests an enhanced detection model to overcome these problems, termed YOLO-CATTLE, based on a YOLO frame...
XinChang Shan, Zhe Jiang, Qi Wu et al.· International Conference on...· 0 citations
In large-scale pig farming systems, high visual similarity among pigs, complex posture variations, and frequent interactions between individuals pose significant challenges to vision-based multi-behavior detection. To overcome these limitations, this study proposes an improved YOLO11-based pig behavior detection model,...
Yan Chen, Jie-Hao Wu, Ying-Jie Kuang et al.· Animals· 0 citations
Simple Summary Individual sheep and goat identification is essential for precise feeding and intelligent management, which not only reduces labor costs but also improves the precision of farm management. A lightweight sheep face detection method based on the RetinaFace framework is proposed in this paper, which simulta...
Fu Zhang, Bao-Ping Yan, Xiao-Peng Zhao et al.· Animals· 0 citations
A YOLOv11-based lightweight weed detection model, termed multi-level feature aggregation-based YOLO (MFA-YOLO), which combines a gated progressive feature fusion (GPFF) module to enhance hierarchical feature interaction via learnable weighting and channel-wise gating, and an efficient feature aggregation (EFA) module t...
Variable illumination, severe occlusion, and the small scale of pest instances in complex forest environments significantly compromise the accuracy of automated forest pest detection. Existing deep learning-based detection models often face challenges in achieving an optimal trade-off among detection accuracy, model li...
As a major staple crop with global significance, maize is highly vulnerable to pest infestations throughout its growth cycle, which can substantially limit plant development and reduce yield. In practice, accurate detection remains challenging due to pronounced morphological variations across pest life stages, as well...
