De Li , Baisheng Dai , Yanxing Li , Peng Song , Xin Dai , Yongqiang He , Huixin Liu , Yang Li , Weizheng Shen
{"title":"IATEFF-YOLO:关注夜间奶牛上架检测","authors":"De Li , Baisheng Dai , Yanxing Li , Peng Song , Xin Dai , Yongqiang He , Huixin Liu , Yang Li , Weizheng Shen","doi":"10.1016/j.biosystemseng.2024.07.017","DOIUrl":null,"url":null,"abstract":"<div><p>Mounting behaviour is an important characteristic of cows during oestrus. Real-time and accurate detection of cow mounting behaviour can shorten the calving-to-conception period and increase the economic benefits for dairy farms. Cow mounting behaviour occurs more often at night, and drastic scale changes in surveillance images caused by different distances between cows and camera, influence the detection of cow mounting. Existing methods are unable to address these challenges effectively. To address these challenges, this study collected 9392 images of Holstein cow mounting behaviour under intensive farming conditions using cameras and proposed an IATEFF-YOLO that is more suitable for cow mounting behaviour detection at nighttime and drastic scale changes in surveillance images caused by different distances between cows and camera. IATEFF-YOLO comprises an Illumination Adaptive Transformer (IAT) and an efficient feature fusion detector. The IAT enhances low-light images at night to enrich the cow mounting features, facilitating the subsequent detection of mounting behaviour. The efficient feature fusion detector, EFF-YOLO, enhances the feature fusion capability and further enable the detector to adapt to drastic scale changes in surveillance images caused by different distances between cows and camera. IATEFF-YOLO achieved a mean Average Precision of 99.3% and a detection speed of 102.0 f/s on test set. Compared with existing methods, IATEFF-YOLO achieved higher detection accuracy and faster detection speed during nighttime and drastic scale changes in surveillance images caused by different distances between cows and camera. IATEFF-YOLO can assist ranch breeders in achieving round-the-clock monitoring of cow oestrus, thereby enhancing oestrus detection efficiency.</p></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"246 ","pages":"Pages 54-66"},"PeriodicalIF":4.4000,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1537511024001703/pdfft?md5=4a8adf4efb993fe66b8d35d36b5d381a&pid=1-s2.0-S1537511024001703-main.pdf","citationCount":"0","resultStr":"{\"title\":\"IATEFF-YOLO: Focus on cow mounting detection during nighttime\",\"authors\":\"De Li , Baisheng Dai , Yanxing Li , Peng Song , Xin Dai , Yongqiang He , Huixin Liu , Yang Li , Weizheng Shen\",\"doi\":\"10.1016/j.biosystemseng.2024.07.017\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Mounting behaviour is an important characteristic of cows during oestrus. Real-time and accurate detection of cow mounting behaviour can shorten the calving-to-conception period and increase the economic benefits for dairy farms. Cow mounting behaviour occurs more often at night, and drastic scale changes in surveillance images caused by different distances between cows and camera, influence the detection of cow mounting. Existing methods are unable to address these challenges effectively. To address these challenges, this study collected 9392 images of Holstein cow mounting behaviour under intensive farming conditions using cameras and proposed an IATEFF-YOLO that is more suitable for cow mounting behaviour detection at nighttime and drastic scale changes in surveillance images caused by different distances between cows and camera. IATEFF-YOLO comprises an Illumination Adaptive Transformer (IAT) and an efficient feature fusion detector. The IAT enhances low-light images at night to enrich the cow mounting features, facilitating the subsequent detection of mounting behaviour. The efficient feature fusion detector, EFF-YOLO, enhances the feature fusion capability and further enable the detector to adapt to drastic scale changes in surveillance images caused by different distances between cows and camera. IATEFF-YOLO achieved a mean Average Precision of 99.3% and a detection speed of 102.0 f/s on test set. Compared with existing methods, IATEFF-YOLO achieved higher detection accuracy and faster detection speed during nighttime and drastic scale changes in surveillance images caused by different distances between cows and camera. IATEFF-YOLO can assist ranch breeders in achieving round-the-clock monitoring of cow oestrus, thereby enhancing oestrus detection efficiency.</p></div>\",\"PeriodicalId\":9173,\"journal\":{\"name\":\"Biosystems Engineering\",\"volume\":\"246 \",\"pages\":\"Pages 54-66\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2024-07-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1537511024001703/pdfft?md5=4a8adf4efb993fe66b8d35d36b5d381a&pid=1-s2.0-S1537511024001703-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biosystems Engineering\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1537511024001703\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURAL ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biosystems Engineering","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1537511024001703","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
IATEFF-YOLO: Focus on cow mounting detection during nighttime
Mounting behaviour is an important characteristic of cows during oestrus. Real-time and accurate detection of cow mounting behaviour can shorten the calving-to-conception period and increase the economic benefits for dairy farms. Cow mounting behaviour occurs more often at night, and drastic scale changes in surveillance images caused by different distances between cows and camera, influence the detection of cow mounting. Existing methods are unable to address these challenges effectively. To address these challenges, this study collected 9392 images of Holstein cow mounting behaviour under intensive farming conditions using cameras and proposed an IATEFF-YOLO that is more suitable for cow mounting behaviour detection at nighttime and drastic scale changes in surveillance images caused by different distances between cows and camera. IATEFF-YOLO comprises an Illumination Adaptive Transformer (IAT) and an efficient feature fusion detector. The IAT enhances low-light images at night to enrich the cow mounting features, facilitating the subsequent detection of mounting behaviour. The efficient feature fusion detector, EFF-YOLO, enhances the feature fusion capability and further enable the detector to adapt to drastic scale changes in surveillance images caused by different distances between cows and camera. IATEFF-YOLO achieved a mean Average Precision of 99.3% and a detection speed of 102.0 f/s on test set. Compared with existing methods, IATEFF-YOLO achieved higher detection accuracy and faster detection speed during nighttime and drastic scale changes in surveillance images caused by different distances between cows and camera. IATEFF-YOLO can assist ranch breeders in achieving round-the-clock monitoring of cow oestrus, thereby enhancing oestrus detection efficiency.
期刊介绍:
Biosystems Engineering publishes research in engineering and the physical sciences that represent advances in understanding or modelling of the performance of biological systems for sustainable developments in land use and the environment, agriculture and amenity, bioproduction processes and the food chain. The subject matter of the journal reflects the wide range and interdisciplinary nature of research in engineering for biological systems.