YOLO-BYTE: An efficient multi-object tracking algorithm for automatic monitoring of dairy cows

•Proposed an efficient and real-time algorithm YOLO-BYTE for multi-object tracking.•Introduced feature extraction module that integrates convolution and self-attention.•Reduced complexity through an optimized Spatial Pyramid Pooling module.•Improved state parameter prediction with the Enhanced ByteT...

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Veröffentlicht in:Computers and electronics in agriculture Jg. 209; S. 107857
Hauptverfasser: Zheng, Zhiyang, Li, Jingwen, Qin, Lifeng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.06.2023
Schlagworte:
agriculture
algorithms
Dairy cow
dairy cows
data collection
electronics
health status
Kalman filter
Multi-object tracking
Object detection
Dairy cow
Multi-object tracking
Kalman filter
Object detection
ISSN:0168-1699, 1872-7107
Online-Zugang:Volltext
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Zusammenfassung:•Proposed an efficient and real-time algorithm YOLO-BYTE for multi-object tracking.•Introduced feature extraction module that integrates convolution and self-attention.•Reduced complexity through an optimized Spatial Pyramid Pooling module.•Improved state parameter prediction with the Enhanced ByteTrack algorithm. Dairy cows tracking is an essential means to obtain their behavioral information, real-time position, activity data, and health status. A multi-object tracking method (YOLO-BYTE) is proposed to address the problem of missed detection and false detection caused by complex environments in cow individual detection and tracking. The method improves upon the YOLO v7 Backbone network feature extraction module by adding a Self-Attention and Convolution mixed module (ACmix) to account for the uneven spatial distribution and target scale variation of the cows. Additionally, in order to reduce the number of model parameters, an improved lightweight Spatial Pyramid Pooling Cross Stage Partial Connections (SPPCSPC-L) module is adopted to reduce model complexity. At the same time, the state parameters in the Kalman filter are improved by directly predicting the width and height information of the tracking boxes, so as to improve the ByteTrack algorithm to make tracking boxes matching the cows more precisely and accurately. Experimental conducted on the dairy cow object detection and multi-object tracking dataset show that the proposed YOLO-BYTE model achieves a Precision (P) of 97.3% in the dairy cow target detection dataset, with an improved Recall (R) and Average Precision (AP) by 1.1% compared to the original algorithm, and an 18% reduction in model parameters. Moreover, the proposed method demonstrated significant improvements in High Order Tracking Accuracy (HOTA), Multi-Object Tracking Accuracy (MOTA), and Identification F1 (IDF1) by 4.4%, 6.1%, and 3.8%, respectively, compared to the original model, with a decrease of 37.5% in Identity Switch (IDS). The tracker runs in a real-time manner with an average analysis speed of 47 fps. Hence, it is demonstrated that the proposed approach is capable of effective multi-object tracking of dairy cows in natural scenes and provides technical support for non-contact dairy cow automatic monitoring.
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ISSN:0168-1699
1872-7107
DOI:10.1016/j.compag.2023.107857