Depth-Oriented Gray Image for Unseen Pig Detection in Real Time

With the increasing demand for pork, improving pig health and welfare management productivity has become a priority. However, it is impractical for humans to manually monitor all pigsties in commercial-scale pig farms, highlighting the need for automated health monitoring systems. In such systems, o...

Full description

Saved in:
Bibliographic Details
Main Authors: Jongwoong Seo, Seungwook Son, Seunghyun Yu, Hwapyeong Baek, Yongwha Chung
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/15/2/988
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1832589163964137472
author Jongwoong Seo
Seungwook Son
Seunghyun Yu
Hwapyeong Baek
Yongwha Chung
author_facet Jongwoong Seo
Seungwook Son
Seunghyun Yu
Hwapyeong Baek
Yongwha Chung
author_sort Jongwoong Seo
collection DOAJ
description With the increasing demand for pork, improving pig health and welfare management productivity has become a priority. However, it is impractical for humans to manually monitor all pigsties in commercial-scale pig farms, highlighting the need for automated health monitoring systems. In such systems, object detection is essential. However, challenges such as insufficient training data, low computational performance, and generalization issues in diverse environments make achieving high accuracy in unseen environments difficult. Conventional RGB-based object detection models face performance limitations due to brightness similarity between objects and backgrounds, new facility installations, and varying lighting conditions. To address these challenges, this study proposes a DOG (Depth-Oriented Gray) image generation method using various foundation models (SAM, LaMa, Depth Anything). Without additional sensors or retraining, the proposed method utilizes depth information from the testing environment to distinguish between foreground and background, generating depth background images and establishing an approach to define the Region of Interest (RoI) and Region of Uninterest (RoU). By converting RGB input images into the HSV color space and combining HSV-Value, inverted HSV-Saturation, and the generated depth background images, DOG images are created to enhance foreground object features while effectively suppressing background information. Experimental results using low-cost CPU and GPU systems demonstrated that DOG images improved detection accuracy (AP50) by up to 6.4% compared to conventional gray images. Moreover, DOG image generation achieved real-time processing speeds, taking 3.6 ms on a CPU, approximately 53.8 times faster than the GPU-based depth image generation time of Depth Anything, which requires 193.7 ms.
format Article
id doaj-art-135fe18298f840e2aea6c56a79e8089d
institution Kabale University
issn 2076-3417
language English
publishDate 2025-01-01
publisher MDPI AG
record_format Article
series Applied Sciences
spelling doaj-art-135fe18298f840e2aea6c56a79e8089d2025-01-24T13:21:35ZengMDPI AGApplied Sciences2076-34172025-01-0115298810.3390/app15020988Depth-Oriented Gray Image for Unseen Pig Detection in Real TimeJongwoong Seo0Seungwook Son1Seunghyun Yu2Hwapyeong Baek3Yongwha Chung4Department of Computer Convergence Software, Korea University, Sejong 30019, Republic of KoreaInfo Valley Korea Co., Ltd., Anyang 14067, Republic of KoreaDepartment of Computer Convergence Software, Korea University, Sejong 30019, Republic of KoreaDepartment of Computer Convergence Software, Korea University, Sejong 30019, Republic of KoreaDepartment of Computer Convergence Software, Korea University, Sejong 30019, Republic of KoreaWith the increasing demand for pork, improving pig health and welfare management productivity has become a priority. However, it is impractical for humans to manually monitor all pigsties in commercial-scale pig farms, highlighting the need for automated health monitoring systems. In such systems, object detection is essential. However, challenges such as insufficient training data, low computational performance, and generalization issues in diverse environments make achieving high accuracy in unseen environments difficult. Conventional RGB-based object detection models face performance limitations due to brightness similarity between objects and backgrounds, new facility installations, and varying lighting conditions. To address these challenges, this study proposes a DOG (Depth-Oriented Gray) image generation method using various foundation models (SAM, LaMa, Depth Anything). Without additional sensors or retraining, the proposed method utilizes depth information from the testing environment to distinguish between foreground and background, generating depth background images and establishing an approach to define the Region of Interest (RoI) and Region of Uninterest (RoU). By converting RGB input images into the HSV color space and combining HSV-Value, inverted HSV-Saturation, and the generated depth background images, DOG images are created to enhance foreground object features while effectively suppressing background information. Experimental results using low-cost CPU and GPU systems demonstrated that DOG images improved detection accuracy (AP50) by up to 6.4% compared to conventional gray images. Moreover, DOG image generation achieved real-time processing speeds, taking 3.6 ms on a CPU, approximately 53.8 times faster than the GPU-based depth image generation time of Depth Anything, which requires 193.7 ms.https://www.mdpi.com/2076-3417/15/2/988foundation modelimage processingpig detectionunseen environmentreal-time application
spellingShingle Jongwoong Seo
Seungwook Son
Seunghyun Yu
Hwapyeong Baek
Yongwha Chung
Depth-Oriented Gray Image for Unseen Pig Detection in Real Time
Applied Sciences
foundation model
image processing
pig detection
unseen environment
real-time application
title Depth-Oriented Gray Image for Unseen Pig Detection in Real Time
title_full Depth-Oriented Gray Image for Unseen Pig Detection in Real Time
title_fullStr Depth-Oriented Gray Image for Unseen Pig Detection in Real Time
title_full_unstemmed Depth-Oriented Gray Image for Unseen Pig Detection in Real Time
title_short Depth-Oriented Gray Image for Unseen Pig Detection in Real Time
title_sort depth oriented gray image for unseen pig detection in real time
topic foundation model
image processing
pig detection
unseen environment
real-time application
url https://www.mdpi.com/2076-3417/15/2/988
work_keys_str_mv AT jongwoongseo depthorientedgrayimageforunseenpigdetectioninrealtime
AT seungwookson depthorientedgrayimageforunseenpigdetectioninrealtime
AT seunghyunyu depthorientedgrayimageforunseenpigdetectioninrealtime
AT hwapyeongbaek depthorientedgrayimageforunseenpigdetectioninrealtime
AT yongwhachung depthorientedgrayimageforunseenpigdetectioninrealtime