ArticleSensors (Basel, Switzerland)2022
GAN-Based Video Denoising with Attention Mechanism for Field-Applicable Pig Detection System.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- StaticPigDetv2: Performance Improvement of Unseen Pig Monitoring Environment Using Depth-Based Background and Facility Information.Sensors (Basel, Switzerland) · 2026Article
- Pattern Mining-Based Pig Behavior Analysis for Health and Welfare Monitoring.Sensors (Basel, Switzerland) · 2024Article
- Pig Movement Estimation by Integrating Optical Flow with a Multi-Object Tracking Model.Sensors (Basel, Switzerland) · 2023Article
- A Method for Obtaining 3D Point Cloud Data by Combining 2D Image Segmentation and Depth Information of Pigs.Animals : an open access journal from MDPI · 2023Article
- Behavior-Based Video Summarization System for Dog Health and Welfare Monitoring.Sensors (Basel, Switzerland) · 2023Article
- StaticPigDet: Accuracy Improvement of Static Camera-Based Pig Monitoring Using Background and Facility Information.Sensors (Basel, Switzerland) · 2022Article
- The Research Progress of Vision-Based Artificial Intelligence in Smart Pig Farming.Sensors (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Infrared cameras allow non-invasive and 24 h continuous monitoring. Thus, they are widely used in automatic pig monitoring, which is essential to maintain the profitability and sustainability of intensive pig farms. However, in practice, impurities such as insect secretions continuously pollute camera lenses. This causes problems with IR reflections, which can seriously affect pig detection performance. In this study, we propose a noise-robust, real-time pig detection system that can improve accuracy in pig farms where infrared cameras suffer from the IR reflection problem. The system consists of a data collector to collect infrared images, a preprocessor to transform noisy images into clean images, and a detector to detect pigs. The preprocessor embeds a multi-scale spatial attention module in U-net and generative adversarial network (GAN) models, enabling the model to pay more attention to the noisy area. The GAN model was trained on paired sets of clean data and data with simulated noise. It can operate in a real-time and end-to-end manner. Experimental results show that the proposed preprocessor was able to significantly improve the average precision of pig detection from 0.766 to 0.906, with an additional execution time of only 4.8 ms on a PC environment.
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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.