文献类型：期刊文献

英文题名：FMRFT: Fusion Mamba and DETR for Query Time Sequence Intersection Fish Tracking

作者：Yao, Mingyuan[1,2,3]; Huo, Yukang[1,2,3]; Tian, Qingbin[1,2,3]; Zhao, Jiayin[1,2,3]; Liu, Xiao[1,2,3]; Wang, Ruifeng[4]; Xue, Lin[5]; Wang, Haihua[1,2,3]

第一作者：Yao, Mingyuan

机构：[1] National Innovation Center for Digital Fishery, No. 17, Qinghua East Road, Haidian District, Beijing, 100083, China; [2] Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs, No. 17, Qinghua East Road, Haidian District, Beijing, 100083, China; [3] College of Information and Electrical Engineering, China Agricultural University, No. 17, Qinghua East Road, Haidian District, Beijing, 10083, China; [4] College of Engineering, China Agricultural University, No. 17, Qinghua East Road, Haidian District, Beijing, 10083, China; [5] Smart City College, Beijing Union University, No. 97, North Fourth Ring East Road, Chaoyang District, Beijing, 100101, China

第一机构：National Innovation Center for Digital Fishery, No. 17, Qinghua East Road, Haidian District, Beijing, 100083, China

年份：2024

外文期刊名：arXiv

收录：EI(收录号：20240395290)

语种：英文

外文关键词：Anomaly detection - Critical path analysis - Deep learning - Fish detectors - Fisheries - Image segmentation - Query processing - Safety devices

摘要：Early detection of abnormal fish behavior caused by disease or hunger can be achieved through fish tracking using deep learning techniques, which holds significant value for industrial aquaculture. However, underwater reflections and some reasons with fish, such as the high similarity, rapid swimming caused by stimuli and mutual occlusion bring challenges to multi-target tracking of fish. To address these challenges, this paper establishes a complex multi-scenario sturgeon tracking dataset and introduces the FMRFT model, a real-time end-to-end fish tracking solution. The model incorporates the low video memory consumption Mamba In Mamba (MIM) architecture, which facilitates multi-frame temporal memory and feature extraction, thereby addressing the challenges to track multiple fish across frames. Additionally, the FMRFT model with the Query Time Sequence Intersection (QTSI) module effectively manages occluded objects and reduces redundant tracking frames using the superior feature interaction and prior frame processing capabilities of RT-DETR. This combination significantly enhances the accuracy and stability of fish tracking. Trained and tested on the dataset, the model achieves an IDF1 score of 90.3% and a MOT A accuracy of 94.3%. Experimental results show that the proposed FMRFT model effectively addresses the challenges of high similarity and mutual occlusion in fish populations, enabling accurate tracking in factory farming environments. Copyright ? 2024, The Authors. All rights reserved.