| 基于DH-YoloX的群养马岗鹅关键行为监测 |
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| 引用本文： | 肖德琴,曾瑞麟,周敏,黄一桂,王文策.基于DH-YoloX的群养马岗鹅关键行为监测[J].农业工程学报,2023,39(2):142-149. |
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| 作者姓名： | 肖德琴 曾瑞麟 周敏 黄一桂 王文策 |
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| 作者单位： | 1. 华南农业大学 农业农村部华南热带智慧农业技术重点实验室，广州 510642；2. 华南农业大学数学与信息学院，广州 510642 |
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| 基金项目： | 国家现代农业产业技术体系资助CARS-42-13 |
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| 摘 要： | 马岗鹅的行为与其生长状况和福利状况密切相关，马岗鹅关键行为监测对评估其生长性能具有重要的现实意义。为了实现对群养栏马岗鹅关键行为高效率精准监测，该研究探索一种基于YoloX的群养马岗鹅关键行为监测算法（Magang geese behavior monitoring of based on Double Head-YoloX,MGBM-DH-YoloX），该算法通过减少YoloX的头部数量提升检测效率、使用损失函数减少前景背景干扰、使用迁移训练方式提高网络训练效率等技术对马岗鹅采食、饮水、休息和应激等关键行为及其规律进行分析。MGBM-DH-YoloX首先用Mosaic和Mixup对马岗鹅图像进行数据增强，然后使用增强后的数据集训练模型，并且利用模型检测马岗鹅的关键行为，最后累计得出马岗鹅关键行为的发生时长和行为节律；试验训练集为1 400幅、验证集200幅和测试集为400幅，连续活动视频10 d。结果表明，MGBM-DH-YoloX算法的平均精度为98.98%、检测速度达到81帧/s、内存消耗为2 520.04 MB。对马岗鹅的10 d养殖数据分析发现，MGBM-DH-YoloX能有...
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| 关 键 词： | 深度学习 算法 卷积神经网络 养殖监测 迁移学习 马岗鹅 |
| 收稿时间： | 2022/10/11 0:00:00 |
| 修稿时间： | 2022/11/15 0:00:00 |
Monitoring the vital behavior of Magang geese raised in flocks based on DH-YoloX |
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| XIAO Deqin,ZENG Ruilin,ZHOU Min,HUANG Yigui,WANG Wence.Monitoring the vital behavior of Magang geese raised in flocks based on DH-YoloX[J].Transactions of the Chinese Society of Agricultural Engineering,2023,39(2):142-149. |
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| Authors: | XIAO Deqin ZENG Ruilin ZHOU Min HUANG Yigui WANG Wence |
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| Institution: | 1. Key Laboratory of Smart Agricultural Technology in Tropical South China Ministry of Agriculture and Rural Affairs, South China Agricultural University, Guangzhou 510642, China; 2. College of Mathematics and Information, South China Agricultural University, Guangzhou, 510642, China |
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| Abstract: | Abstract: The vital behaviors of Magang geese are closely related to their growth and welfare status. Therefore, it is very necessary to accurately identify the key vital behaviors of Magang geese in husbandry and production. This study aims to efficiently and accurately monitor the key behaviors of Magang geese in pens. An improved algorithm was also proposed for the vital behavior of Magang geese using Double Head-YoloX (referred to as MGBM-DH-YoloX). The detection efficiency of the network was enhanced to reduce the number of YoloX heads. A tradeoff was made to balance the foreground and background using the Focal loss objective function. Efficient network training was achieved using migration training. Other techniques were utilized to identify the vital behavior and count their key behaviors once at a fixed time in an efficient and accurate way, including the identification and rule analysis of key behaviors, such as feeding, drinking, rest and stress of Magang Geese. Firstly, MGBM-DH-YoloX enhanced the data of the Magang geese images with Mosaic and Mixup. And then the enhanced dataset was used to train the DH-YoloX (Double Head - YoloX) for the detection of the vital behavior of the waterfowl. Finally, the vital behavior of the Magang geese was counted every 25 frames. The experiment was conducted in a flock-reared environment with the Magang geese as the subject of the case study. A behavioral target detection test was conducted during this time. Waterfowl videos were collected on multiple days from 26 December 2021 to 04 January 2022, and from 10-12 May 2022 at the Nanwei Building Experimental Farm, School of Animal Science, South China Agricultural University, Tianhe District, Guangzhou City, Guangdong Province, China. A platform was also built to acquire the data for the behavior detection of geese. The color images of Magang geese were facilitated to integrate the environmental factors in the vicinity of the breeding pen (the height of the pen, as well as the width and height of the equipment box). The bi-directional difference frame was adopted to extract the key frames, in order to quickly extract the required picture production dataset from the video data. A total of 1 600 picture data was extracted from the video data, and selected for a total of 2 000 picture data. A total of 1 600 images were randomly used for the training and validation sets, whereas, 400 images were for the test set in the 10 days of continuous activity video. The results showed that the MGBM-DH-YoloX algorithm achieved an average accuracy of 98.98% mAP, a processing frame rate of 81 frame/s, and a memory consumption of 2 520.04 MB for the detection of Magang geese''s behaviors. Meanwhile, it was found that the geese were foraged less frequently, as they grow older, after monitoring the 10-day breeding data. The vital foraging and drinking behaviors simultaneously accounted for 83.74% of the total foraging time per day, indicating the overall companionship trend. There was also a decrease from 90.78% to 74.57%, indicating the gradual separation from the feeding and drinking behavior of Magang Geese with the increase of age. On the contrary, the resting behavior of the experimental geese increased slowly with the increase of age, indicating a gradual adaptation to the caging. The stress behavior was highly random in this experiment. There was an extremely serious emergency caused by irregular feeding, such as the random walking of personnel. Consequently, the MGBM-DH-YoloX can be expected as the video monitoring to intelligently extract the vital behavior of Magang geese. The finding can provide technical support for the automated monitoring of poultry in intelligent breeding supervision. |
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| Keywords: | deep learning algorithm convolutional neural networks breeding monitoring migration learning Magang''s geese |
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