Aiming at the difficulties of the health status recognition of yellow feather broilers in large-scale broiler farms and the low recognition rate of current models,a novel method based on machine vision to achieve prec...Aiming at the difficulties of the health status recognition of yellow feather broilers in large-scale broiler farms and the low recognition rate of current models,a novel method based on machine vision to achieve precise tracking of multiple broilers was proposed in this paper.Broilers’behavior in the breeding environment can be tracked to analyze their behaviors and health status further.An improved YOLOv3(You Only Look Once v3)algorithm was used as the detector of the Deep SORT(Simple Online and Realtime Tracking)algorithm to realize the multiple object tracking of yellow feather broilers in the flat breeding chamber,which replaced the backbone of YOLOv3 with MobileNetV2 to improve the inference speed of the detection module.The DRSN(Deep Residual Shrinkage Network)was integrated with MobileNetV2 to enhance the feature extraction capability of the network.Moreover,in view of the slight change in the individual size of the yellow feather broiler,the feature fusion network was also redesigned by combining it with the attention mechanism to enable the adaptive learning of the objects’multi-scale features.Compared with traditional YOLOv3,improved YOLOv3 achieves 93.2%mAP(mean Average Precision)and 29 fps(frames per second),representing high-precision real-time detection performance.Furthermore,while the MOTA(Multiple Object Tracking Accuracy)increases from 51%to 54%,the IDSW(Identity Switch)decreases by 62.2%compared with traditional YOLOv3-based objective detectors.The proposed algorithm can provide a technical reference for analyzing the behavioral perception and health status of broilers in the flat breeding environment.展开更多
煤矿井下安全生产是保障矿工生命安全和能源稳定供应的核心环节,但传统监控方法在检测实时性和准确性方面存在明显不足。针对复杂井下环境中人员入侵识别精度低的问题,研究提出一种基于改进You Only Look Once version 5(YOLOv5)和深度...煤矿井下安全生产是保障矿工生命安全和能源稳定供应的核心环节,但传统监控方法在检测实时性和准确性方面存在明显不足。针对复杂井下环境中人员入侵识别精度低的问题,研究提出一种基于改进You Only Look Once version 5(YOLOv5)和深度简单在线实时跟踪算法的电子围栏入侵检测技术。研究通过嵌入注意力机制增强模型对关键特征的感知能力,并利用扩展卡尔曼滤波与匈牙利算法提升跟踪稳定性。实验结果表明,改进后的模型的识别率最高,且随迭代次数的增加其识别率始终在90%以上。该技术在识别性能上,平均精度均值指标为92.5%,每秒帧数提升至116.7,训练时长缩短至8.5 h,漏检率显著降低。研究表明,该方法在光照不均、煤尘干扰等复杂场景下具备更高的检测精度与实时性。该技术的提出可为煤矿井下智能化安全管理提供有效技术支撑,从而提高煤矿安全生产水平,杜绝生产事故出现。展开更多
基金funded by Jiangsu Agriculture Science and Technology Innovation Fund(Grant No.CX(21)3058)Xuzhou Key Research and Development Project(Modern Agriculture)(Grant No.KC21135)International Science and Technology Cooperation Program of Jiangsu Province(Grant No.BZ2023013).
文摘Aiming at the difficulties of the health status recognition of yellow feather broilers in large-scale broiler farms and the low recognition rate of current models,a novel method based on machine vision to achieve precise tracking of multiple broilers was proposed in this paper.Broilers’behavior in the breeding environment can be tracked to analyze their behaviors and health status further.An improved YOLOv3(You Only Look Once v3)algorithm was used as the detector of the Deep SORT(Simple Online and Realtime Tracking)algorithm to realize the multiple object tracking of yellow feather broilers in the flat breeding chamber,which replaced the backbone of YOLOv3 with MobileNetV2 to improve the inference speed of the detection module.The DRSN(Deep Residual Shrinkage Network)was integrated with MobileNetV2 to enhance the feature extraction capability of the network.Moreover,in view of the slight change in the individual size of the yellow feather broiler,the feature fusion network was also redesigned by combining it with the attention mechanism to enable the adaptive learning of the objects’multi-scale features.Compared with traditional YOLOv3,improved YOLOv3 achieves 93.2%mAP(mean Average Precision)and 29 fps(frames per second),representing high-precision real-time detection performance.Furthermore,while the MOTA(Multiple Object Tracking Accuracy)increases from 51%to 54%,the IDSW(Identity Switch)decreases by 62.2%compared with traditional YOLOv3-based objective detectors.The proposed algorithm can provide a technical reference for analyzing the behavioral perception and health status of broilers in the flat breeding environment.
文摘煤矿井下安全生产是保障矿工生命安全和能源稳定供应的核心环节,但传统监控方法在检测实时性和准确性方面存在明显不足。针对复杂井下环境中人员入侵识别精度低的问题,研究提出一种基于改进You Only Look Once version 5(YOLOv5)和深度简单在线实时跟踪算法的电子围栏入侵检测技术。研究通过嵌入注意力机制增强模型对关键特征的感知能力,并利用扩展卡尔曼滤波与匈牙利算法提升跟踪稳定性。实验结果表明,改进后的模型的识别率最高,且随迭代次数的增加其识别率始终在90%以上。该技术在识别性能上,平均精度均值指标为92.5%,每秒帧数提升至116.7,训练时长缩短至8.5 h,漏检率显著降低。研究表明,该方法在光照不均、煤尘干扰等复杂场景下具备更高的检测精度与实时性。该技术的提出可为煤矿井下智能化安全管理提供有效技术支撑,从而提高煤矿安全生产水平,杜绝生产事故出现。
