ArticleSensors (Basel, Switzerland)2018
A Kinect-Based Segmentation of Touching-Pigs for Real-Time Monitoring.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Article
- Sustainable Self-Training Pig Detection System with Augmented Single Labeled Target Data for Solving Domain Shift Problem.Sensors (Basel, Switzerland) · 2025Article
- RGB-based machine vision for enhanced pig disease symptoms monitoring and health management: a review.Journal of animal science and technology · 2025Review
- Research on Dynamic Pig Counting Method Based on Improved YOLOv7 Combined with DeepSORT.Animals : an open access journal from MDPI · 2024Article
- Automatic Detection and Segmentation for Group-Housed Pigs Based on PigMS R-CNN.Sensors (Basel, Switzerland) · 2021Article
- Panoptic Segmentation of Individual Pigs for Posture Recognition.Sensors (Basel, Switzerland) · 2020Article
- Long-Term Tracking of Group-Housed Livestock Using Keypoint Detection and MAP Estimation for Individual Animal Identification.Sensors (Basel, Switzerland) · 2020Article
- Deep Learning and Machine Vision Approaches for Posture Detection of Individual Pigs.Sensors (Basel, Switzerland) · 2019Article
- Multi-Pig Part Detection and Association with a Fully-Convolutional Network.Sensors (Basel, Switzerland) · 2019Article
- Graph Cut-Based Human Body Segmentation in Color Images Using Skeleton Information from the Depth Sensor.Sensors (Basel, Switzerland) · 2019Article
- Non-Contact Body Measurement for Qinchuan Cattle with LiDAR Sensor.Sensors (Basel, Switzerland) · 2018Article
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Authors and funding
7 authors.
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Abstract
Segmenting touching-pigs in real-time is an important issue for surveillance cameras intended for the 24-h tracking of individual pigs. However, methods to do so have not yet been reported. We particularly focus on the segmentation of touching-pigs in a crowded pig room with low-contrast images obtained using a Kinect depth sensor. We reduce the execution time by combining object detection techniques based on a convolutional neural network (CNN) with image processing techniques instead of applying time-consuming operations, such as optimization-based segmentation. We first apply the fastest CNN-based object detection technique (i.e., You Only Look Once, YOLO) to solve the separation problem for touching-pigs. If the quality of the YOLO output is not satisfied, then we try to find the possible boundary line between the touching-pigs by analyzing the shape. Our experimental results show that this method is effective to separate touching-pigs in terms of both accuracy (i.e., 91.96%) and execution time (i.e., real-time execution), even with low-contrast images obtained using a Kinect depth sensor.
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