Dynamic Object Detection and Non-Contact Localization in Lightweight Cattle Farms Based on Binocular Vision and Improved YOLOv8s

文献类型： 外文期刊

第一作者： Li, Shijie

作者： Li, Shijie;Li, Shijie;Cao, Shanshan;Wei, Peigang;Sun, Wei;Li, Shijie;Li, Shijie;Kong, Fantao

作者机构：

关键词： machine vision localization; dynamic object detection; binocular stereo vision; non-contact; lightweight; YOLOv8s

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 16 期

页码：

收录情况： SCI

摘要： The real-time detection and localization of dynamic targets in cattle farms are crucial for the effective operation of intelligent equipment. To overcome the limitations of wearable devices, including high costs and operational stress, this paper proposes a lightweight, non-contact solution. The goal is to improve the accuracy and efficiency of target localization while reducing the complexity of the system. A novel approach is introduced based on YOLOv8s, incorporating a C2f_DW_StarBlock module. The system fuses binocular images from a ZED2i camera with GPS and IMU data to form a multimodal ranging and localization module. Experimental results demonstrate a 36.03% reduction in model parameters, a 33.45% decrease in computational complexity, and a 38.67% reduction in model size. The maximum ranging error is 4.41%, with localization standard deviations of 1.02 m (longitude) and 1.10 m (latitude). The model is successfully integrated into an ROS system, achieving stable real-time performance. This solution offers the advantages of being lightweight, non-contact, and low-maintenance, providing strong support for intelligent farm management and multi-target monitoring.

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