Origin paper
Crowd-aware Black Pig Detection for Low Illumination
Towards automatic farrowing monitoring—A Noisy Student approach for improving detection performance of newborn piglets
A lightweight enhanced YOLOv8 algorithm for detecting small objects in UAV aerial photography
Small target detection in UAV view based on improved YOLOv8 algorithm
YOLO-Type Neural Networks in the Process of Adapting Mathematical Graphs to the Needs of the Blind
Small object detection algorithm based on improved YOLOv8s for UAV viewpoints
Pig Health Abnormality Detection Based on Behavior Patterns in Activity Periods using Deep Learning
An efficient anchor-free method for pig detection
DAT-YOLO: Small Object Detection Model from the Perspective of Drones
Dual attention-guided feature pyramid network for instance segmentation of group pigs
EmbeddedPigCount: Pig Counting with Video Object Detection and Tracking on an Embedded Board
Research on Pattern Recognition Technology Based on Computer Graphics Attention Mechanism – Taking the Improved YOLOv5 Model as an Example
StaticPigDet: Accuracy Improvement of Static Camera-Based Pig Monitoring Using Background and Facility Information
EnsemblePigDet: Ensemble Deep Learning for Accurate Pig Detection
An Examination of the Feasibility of Various Deep Learning Object Detecting Techniques
An Efficient Grocery Detection System Using HYOLO-NAS Deep Learning Model for Visually Impaired People
Improved glove defect detection algorithm based on YOLOv5 framework
Neural network developments: A detailed survey from static to dynamic models
Sign Language Translation and Voice Impairment Support System using Deep Learning
User Interface based Text-To-Speech Synthesizer
Efficient Real-time Book Cover Text Extraction with YOLOv8 and SpaCy
Retail Enhancement Using Computer Vision Models
Performance Evaluation of YOLOv8 and YOLOv9 for Object Detection in Remote Sensing Images
YOLOv5-KCB: A New Method for Individual Pig Detection Using Optimized K-Means, CA Attention Mechanism and a Bi-Directional Feature Pyramid Network
Automated Individual Pig Localisation, Tracking and Behaviour Metric Extraction Using Deep Learning
EmbeddedPigDet—Fast and Accurate Pig Detection for Embedded Board Implementations
Yolov5-based defect detection for wafer surface micropipe
RPS-YOLO: A Recursive Pyramid Structure-Based YOLO Network for Small Object Detection in Unmanned Aerial Vehicle Scenarios
Tracking Grow-Finish Pigs Across Large Pens Using Multiple Cameras
Research on improved YOLOv8 remote sensing target detection algorithm based on multi-receptive field feature enhancement
YOLOv5-SA-FC: A Novel Pig Detection and Counting Method Based on Shuffle Attention and Focal Complete Intersection over Union
Long-term video activity monitoring and anomaly alerting of group-housed pigs
Object Detection Algorithm for UAV Aerial Images Based on UAV-YOLO
YOLO-OD: Obstacle Detection for Visually Impaired Navigation Assistance
A pig tracking algorithm with improved IOU-tracker
Towards re-identification for long-term tracking of group housed pigs
Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs
Estimation of Number of Pigs Taking in Feed Using Posture Filtration
An Intelligent Pig Weights Estimate Method Based on Deep Learning in Sow Stall Environments
Revolutionizing Rose Grading: Real-Time Detection and Accurate Assessment with YOLOv8 and Deep Learning Models
Deep-Learning-Based Automatic Monitoring of Pigs’ Physico-Temporal Activities at Different Greenhouse Gas Concentrations
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20192025
Automated Individual Pig Localisation, Tracking and Behaviour Metric Extraction Using Deep LearningAutomated recognition of postures and drinking behaviour for the detection of compromised health in pigsAn Intelligent Pig Weights Estimate Method Based on Deep Learning in Sow Stall EnvironmentsEmbeddedPigDet—Fast and Accurate Pig Detection for Embedded Board ImplementationsDual attention-guided feature pyramid network for instance segmentation of group pigsTowards re-identification for long-term tracking of group housed pigsDeep-Learning-Based Automatic Monitoring of Pigs’ Physico-Temporal Activities at Different Greenhouse Gas ConcentrationsEmbeddedPigCount: Pig Counting with Video Object Detection and Tracking on an Embedded BoardEnsemblePigDet: Ensemble Deep Learning for Accurate Pig DetectionYOLOv5-KCB: A New Method for Individual Pig Detection Using Optimized K-Means, CA Attention Mechanism and a Bi-Directional Feature Pyramid NetworkYOLOv5-SA-FC: A Novel Pig Detection and Counting Method Based on Shuffle Attention and Focal Complete Intersection over UnionPig Health Abnormality Detection Based on Behavior Patterns in Activity Periods using Deep LearningEstimation of Number of Pigs Taking in Feed Using Posture FiltrationTracking Grow-Finish Pigs Across Large Pens Using Multiple CamerasA pig tracking algorithm with improved IOU-trackerStaticPigDet: Accuracy Improvement of Static Camera-Based Pig Monitoring Using Background and Facility InformationLong-term video activity monitoring and anomaly alerting of group-housed pigsSmall target detection in UAV view based on improved YOLOv8 algorithmImproved glove defect detection algorithm based on YOLOv5 frameworkAn efficient anchor-free method for pig detectionNeural network developments: A detailed survey from static to dynamic modelsYolov5-based defect detection for wafer surface micropipeRevolutionizing Rose Grading: Real-Time Detection and Accurate Assessment with YOLOv8 and Deep Learning ModelsYOLO-Type Neural Networks in the Process of Adapting Mathematical Graphs to the Needs of the BlindTowards automatic farrowing monitoring—A Noisy Student approach for improving detection performance of newborn pigletsYOLO-OD: Obstacle Detection for Visually Impaired Navigation AssistanceCrowd-aware Black Pig Detection for Low IlluminationA lightweight enhanced YOLOv8 algorithm for detecting small objects in UAV aerial photographyAn Efficient Grocery Detection System Using HYOLO-NAS Deep Learning Model for Visually Impaired PeopleSign Language Translation and Voice Impairment Support System using Deep LearningUser Interface based Text-To-Speech SynthesizerAn Examination of the Feasibility of Various Deep Learning Object Detecting TechniquesEfficient Real-time Book Cover Text Extraction with YOLOv8 and SpaCyRetail Enhancement Using Computer Vision ModelsPerformance Evaluation of YOLOv8 and YOLOv9 for Object Detection in Remote Sensing ImagesResearch on Pattern Recognition Technology Based on Computer Graphics Attention Mechanism – Taking the Improved YOLOv5 Model as an ExampleObject Detection Algorithm for UAV Aerial Images Based on UAV-YOLORPS-YOLO: A Recursive Pyramid Structure-Based YOLO Network for Small Object Detection in Unmanned Aerial Vehicle ScenariosDAT-YOLO: Small Object Detection Model from the Perspective of DronesSmall object detection algorithm based on improved YOLOv8s for UAV viewpointsResearch on improved YOLOv8 remote sensing target detection algorithm based on multi-receptive field feature enhancementCowton, 2019Alameer, 2020Cang, 2019Seo, 2020Hu, 2021Wang, 2022Bhujel, 2021Kim, 2022Ahn, 2021Li, 2023Hao, 2023Tran, 2023Kim, 2022Shirke, 2021Gong, 2022Son, 2022Yang, 2024Zhang, 2025Wang, 2022Mattina, 2022Verma, 2024Zhou, 2022Tasnim, 2024Kawulok, 2024Wutke, 2024Wang, 2024Zhang, 2022Pan, 2025Chhabra, 2024J, 2024Khandelwal, 2024Chhabra, 2023Alghemei, 2024Zgorni, 2024Reda, 2024Niu, 2022Liu, 2024Lei, 2025Li, 2025Chen, 2025Wang, 2024Cowton, 2019Alameer, 2020Cang, 2019Seo, 2020Hu, 2021Wang, 2022Bhujel, 2021Kim, 2022Ahn, 2021Li, 2023Hao, 2023Tran, 2023Kim, 2022Shirke, 2021Gong, 2022Son, 2022Yang, 2024Zhang, 2025Wang, 2022Mattina, 2022Verma, 2024Zhou, 2022Tasnim, 2024Kawulok, 2024Wutke, 2024Wang, 2024Zhang, 2022Pan, 2025Chhabra, 2024J, 2024Khandelwal, 2024Chhabra, 2023Alghemei, 2024Zgorni, 2024Reda, 2024Niu, 2022Liu, 2024Lei, 2025Li, 2025Chen, 2025Wang, 2024
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Pig detection is vital to pig farms since it is the basis for counting, weight estimation, and behavior recognition functions. Existing methods focused on white pig detection instead of black pigs because the contrast between white pigs and the background is more evident than black pigs, making black pig detection more challenging. Furthermore, pig farms often suffer from insufficient light, and pigs tend to be stacked together, which makes it hard to detect pigs accurately. To this end, we propose a black pig detection method, based on YOLOV4, robust to both crowd scenes and low-illumination conditions. The method consists of the following parts: 1) generate domain adaptation dataset (DAD) based on style-transfer to optimize the original data distribution, therefore improving the performance of the method for low-illuminated conditions; 2) propose a crowd-aware module (CAM), adapted to YOLOV4 backbone architecture, to generate crowd density maps; 3) develop an adaptive attention module (AAM) to fuse YOLOV4 backbone features with corresponding crowd density map allowing the method robust to pigs in the crowd. The experimental results confirm the feasibility of this method. The mAP value in fully-lighting and poorly-lighting increased to 88.95% and 84.74%, respectively.