文献类型： 外文期刊

第一作者： Zhao, Shida

作者： Zhao, Shida;Bai, Zongchun;Huo, Lianfei;Han, Guofeng;Duan, Enze;Zhao, Shida;Bai, Zongchun;Huo, Lianfei;Han, Guofeng;Duan, Enze;Gong, Dongjun;Gao, Liaoyuan

作者机构：

关键词： cage-reared; meat duck; attention mechanism; abnormal detection; pose estimation

期刊名称：ANIMALS （ 影响因子：2.7； 五年影响因子：3.0 ）

ISSN： 2076-2615

年卷期： 2024 年 14 卷 15 期

页码：

收录情况： SCI

摘要： 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.

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