Next Article in Journal
Rabies in the Endemic Region of Algeria: Knowledge, Attitude and Practice (KAP) Survey among University Students
Previous Article in Journal
Comparative Analysis of Morphometric, Densitometric, and Mechanical Properties of Skeletal Locomotor Elements in Three Duck Species (Anatidae: Anatinae)
Previous Article in Special Issue
Improved Chinese Giant Salamander Parental Care Behavior Detection Based on YOLOv8
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Automatic Perception of Typical Abnormal Situations in Cage-Reared Ducks Using Computer Vision

1
Institute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
2
Key Laboratory of Protected Agriculture Engineering in the Middle and Lower Reaches of Yangtze River, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
3
College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
4
State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang 471039, China
*
Author to whom correspondence should be addressed.
Animals 2024, 14(15), 2192; https://doi.org/10.3390/ani14152192 (registering DOI)
Submission received: 21 June 2024 / Revised: 22 July 2024 / Accepted: 25 July 2024 / Published: 27 July 2024

Simple Summary

Efficient breeding of meat ducks using three-dimensional and multi-layer cages is a novel approach being actively explored in China. In this process, timely and accurate detection of abnormal situations among ducks is crucial for optimizing and refining the cage-rearing system, and ensuring animal health and welfare. This study focused on the overturned and dead status of cage-reared ducks using YOLOv8 as the basic network. By introducing GAM and Wise-IoU loss functions, we proposed an abnormal-situation recognition method for cage-reared ducks based on YOLOv8-ACRD. Building on this, we refined the identification of key body parts of cage-reared ducks, focusing on six key points: head, beak, chest, tail, left foot, and right foot. This resulted in the development of an abnormal posture estimation model for cage-reared ducks, based on HRNet-48. Furthermore, through multiple tests and comparative verification experiments, it was confirmed that the proposed method exhibited high detection accuracy, generalization ability, and robust comprehensive performance. The method proposed in this study for perceiving abnormal situations in cage-reared ducks not only provides foundational information for the progress and improvement of the meat duck cage-reared system but also offers technological references for the intelligent breeding of other cage-reared poultry.

Abstract

Overturning and death are common abnormalities in cage-reared ducks. To achieve timely and accurate detection, this study focused on 10-day-old cage-reared ducks, which are prone to these conditions, and established prior data on such situations. Using the original YOLOv8 as the base network, multiple GAM attention mechanisms were embedded into the feature fusion part (neck) to enhance the network’s focus on the abnormal regions in images of cage-reared ducks. Additionally, the Wise-IoU loss function replaced the CIoU loss function by employing a dynamic non-monotonic focusing mechanism to balance the data samples and mitigate excessive penalties from geometric parameters in the model. The image brightness was adjusted by factors of 0.85 and 1.25, and mainstream object-detection algorithms were adopted to test and compare the generalization and performance of the proposed method. Based on six key points around the head, beak, chest, tail, left foot, and right foot of cage-reared ducks, the body structure of the abnormal ducks was refined. Accurate estimation of the overturning and dead postures was achieved using the HRNet-48. The results demonstrated that the proposed method accurately recognized these states, achieving a mean Average Precision (mAP) value of 0.924, which was 1.65% higher than that of the original YOLOv8. The method effectively addressed the recognition interference caused by lighting differences, and exhibited an excellent generalization ability and comprehensive detection performance. Furthermore, the proposed abnormal cage-reared duck pose-estimation model achieved an Object Key point Similarity (OKS) value of 0.921, with a single-frame processing time of 0.528 s, accurately detecting multiple key points of the abnormal cage-reared duck bodies and generating correct posture expressions.
Keywords: cage-reared; meat duck; attention mechanism; abnormal detection; pose estimation cage-reared; meat duck; attention mechanism; abnormal detection; pose estimation

Share and Cite

MDPI and ACS Style

Zhao, S.; Bai, Z.; Huo, L.; Han, G.; Duan, E.; Gong, D.; Gao, L. Automatic Perception of Typical Abnormal Situations in Cage-Reared Ducks Using Computer Vision. Animals 2024, 14, 2192. https://doi.org/10.3390/ani14152192

AMA Style

Zhao S, Bai Z, Huo L, Han G, Duan E, Gong D, Gao L. Automatic Perception of Typical Abnormal Situations in Cage-Reared Ducks Using Computer Vision. Animals. 2024; 14(15):2192. https://doi.org/10.3390/ani14152192

Chicago/Turabian Style

Zhao, Shida, Zongchun Bai, Lianfei Huo, Guofeng Han, Enze Duan, Dongjun Gong, and Liaoyuan Gao. 2024. "Automatic Perception of Typical Abnormal Situations in Cage-Reared Ducks Using Computer Vision" Animals 14, no. 15: 2192. https://doi.org/10.3390/ani14152192

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop