{"title":"Detection of attack behaviour of pig based on deep learning","authors":"Yanwen Li, Juxia Li, Tengxiao Na, Hua Yang","doi":"10.1080/21642583.2023.2249934","DOIUrl":null,"url":null,"abstract":"Attack behaviour detection of the pig is a valid method to protect the health of pig. Due to the farm conditions and the illumination changes of the piggery, the images of the pig in the videos are often being overlapped, which lead to difficulties in recognizing pig attack behaviour. We propose an improved YOLOX target detection model to overcome these difficulties. The improvements of the proposed model are: (1) the normalization attention mechanism is adopted to gain global information in the last block of the neck network and (2) the loss function IoU in YOLOX is replaced by DIoU to improve the detection accuracy. The pig attack behaviour considered in this paper includes the ear biting, the tail biting, the head to head collision and the head to body collision. The dataset is builded from the artificially observed attack video segments by using the inter-frame difference method. In the pig attack behaviour detection experiments, the improved YOLOX model achieves 93.21% precision which is 5.30% higher than the YOLOX model. The experiment results show that the improved YOLOX can realize pig attack behaviour detection with high precision.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems Science & Control Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/21642583.2023.2249934","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Attack behaviour detection of the pig is a valid method to protect the health of pig. Due to the farm conditions and the illumination changes of the piggery, the images of the pig in the videos are often being overlapped, which lead to difficulties in recognizing pig attack behaviour. We propose an improved YOLOX target detection model to overcome these difficulties. The improvements of the proposed model are: (1) the normalization attention mechanism is adopted to gain global information in the last block of the neck network and (2) the loss function IoU in YOLOX is replaced by DIoU to improve the detection accuracy. The pig attack behaviour considered in this paper includes the ear biting, the tail biting, the head to head collision and the head to body collision. The dataset is builded from the artificially observed attack video segments by using the inter-frame difference method. In the pig attack behaviour detection experiments, the improved YOLOX model achieves 93.21% precision which is 5.30% higher than the YOLOX model. The experiment results show that the improved YOLOX can realize pig attack behaviour detection with high precision.
期刊介绍:
Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory