ArticleSensors (Basel, Switzerland)2023
An Improved Pig Counting Algorithm Based on YOLOv5 and DeepSORT Model.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 21 citations in OpenAlex.
- An Automated Population Monitoring Framework forSensors (Basel, Switzerland) · 2026Article
- Computer Vision Applied to the Analysis of Pig Behavior Patterns in an Air-Conditioned Environment.Animals : an open access journal from MDPI · 2026Article
- Pig Health Assessment Framework Based on Behavioural Analysis.Animals : an open access journal from MDPI · 2025Article
- Enhanced YOLOv8 for Robust Pig Detection and Counting in Complex Agricultural Environments.Animals : an open access journal from MDPI · 2025Article
- SDGTrack: A Multi-Target Tracking Method for Pigs in Multiple Farming Scenarios.Animals : an open access journal from MDPI · 2025Article
- Research on Enhanced Dynamic Pig Counting Based on YOLOv8n and Deep SORT.Sensors (Basel, Switzerland) · 2025Article
- Potato precision planter metering system based on improved YOLOv5n-ByteTrack.Frontiers in plant science · 2025Article
- Behavior Tracking and Analyses of Group-Housed Pigs Based on Improved ByteTrack.Animals : an open access journal from MDPI · 2024Article
- Research on Cattle Behavior Recognition and Multi-Object Tracking Algorithm Based on YOLO-BoT.Animals : an open access journal from MDPI · 2024Article
- A Long-Term Video Tracking Method for Group-Housed Pigs.Animals : an open access journal from MDPI · 2024Article
- Research on Dynamic Pig Counting Method Based on Improved YOLOv7 Combined with DeepSORT.Animals : an open access journal from MDPI · 2024Article
- Determining the posture and location of pigs using an object detection model under different lighting conditions.Translational animal science · 2024Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Pig counting is an important task in pig sales and breeding supervision. Currently, manual counting is low-efficiency and high-cost and presents challenges in terms of statistical analysis. In response to the difficulties faced in pig part feature detection, the loss of tracking due to rapid movement, and the large counting deviation in pig video tracking and counting research, this paper proposes an improved pig counting algorithm (Mobile Pig Counting Algorithm with YOLOv5xpig and DeepSORTPig (MPC-YD)) based on YOLOv5 + DeepSORT model. The algorithm improves the detection rate of pig body parts by adding two different sizes of SPP networks and using SoftPool instead of MaxPool operations in YOLOv5x. In addition, the algorithm includes a pig reidentification network, a pig-tracking method based on spatial state correction, and a pig counting method based on frame number judgment on the DeepSORT algorithm to improve pig tracking accuracy. Experimental analysis shows that the MPC-YD algorithm achieves an average precision of 99.24% in pig object detection and an accuracy of 85.32% in multitarget pig tracking. In the aisle environment of the slaughterhouse, the MPC-YD algorithm achieves a correlation coefficient (R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.