While moving ahead with the object detection technology, especially deep neural networks, many related tasks, such as medical application and industrial automation, have achieved great success. However, the detection ...While moving ahead with the object detection technology, especially deep neural networks, many related tasks, such as medical application and industrial automation, have achieved great success. However, the detection of objects with multiple aspect ratios and scales is still a key problem. This paper proposes a top-down and bottom-up feature pyramid network(TDBU-FPN),which combines multi-scale feature representation and anchor generation at multiple aspect ratios. First, in order to build the multi-scale feature map, this paper puts a number of fully convolutional layers after the backbone. Second, to link neighboring feature maps, top-down and bottom-up flows are adopted to introduce context information via top-down flow and supplement suboriginal information via bottom-up flow. The top-down flow refers to the deconvolution procedure, and the bottom-up flow refers to the pooling procedure. Third, the problem of adapting different object aspect ratios is tackled via many anchor shapes with different aspect ratios on each multi-scale feature map. The proposed method is evaluated on the pattern analysis, statistical modeling and computational learning visual object classes(PASCAL VOC)dataset and reaches an accuracy of 79%, which exhibits a 1.8% improvement with a detection speed of 23 fps.展开更多
在钢铁行业中,碳化物是钢材中一种非常重要的组成成分,其在钢材中的分布对于评估钢材的质量具有很高的参考价值。然而,目前棒材碳化物的检测手段主要为人工检测,成本高昂且缺乏稳定性。引入人工智能领域的深度学习技术,收集并标注了319...在钢铁行业中,碳化物是钢材中一种非常重要的组成成分,其在钢材中的分布对于评估钢材的质量具有很高的参考价值。然而,目前棒材碳化物的检测手段主要为人工检测,成本高昂且缺乏稳定性。引入人工智能领域的深度学习技术,收集并标注了3192张高质量钢铁棒材带状碳化物图像与11个完整样品数据,创建了工业场景下的棒材带状碳化物目标检测数据集(Banded carbide dataset on object detection for steel bar,BCDOD)。使用深度学习领域中常见的目标检测方法对数据集进行了实验分析,针对应用场景与数据的特点,引入旋转数据增强、Focal Loss函数与负样本微调对级联R⁃CNN模型进行改进,提升了模型的性能,平均精度达到96%。同时,在完整样品数据取得了100%的识别准确率,取得了较为理想的效果,弥补了人工智能技术在碳化物金相检测领域的空缺。展开更多
针对光照不均、噪声大、拍摄质量不高的夜晚水下环境,为实现夜晚水下图像中鱼类目标的快速检测,利用计算机视觉技术,提出了一种基于改进Cascade R-CNN算法和具有色彩保护的MSRCP(Multi-scale Retinex with color restoration)图像增强...针对光照不均、噪声大、拍摄质量不高的夜晚水下环境,为实现夜晚水下图像中鱼类目标的快速检测,利用计算机视觉技术,提出了一种基于改进Cascade R-CNN算法和具有色彩保护的MSRCP(Multi-scale Retinex with color restoration)图像增强算法的夜晚水下鱼类目标检测方法。首先针对夜晚水下环境的视频数据,根据时间间隔,截取出相应的夜晚水下鱼类图像,对截取的原始图像进行MSRCP图像增强。然后采用DetNASNet主干网络进行网络训练和水下鱼类特征信息的提取,将提取出的特征信息输入到Cascade R-CNN模型中,并使用Soft-NMS候选框优化算法对其中的RPN网络进行优化,最后对夜晚水下鱼类目标进行检测。实验结果表明,该方法解决了夜晚水下环境中的图像降质、鱼类目标重叠检测问题,实现了对夜晚水下鱼类目标的快速检测,对夜晚水下鱼类图像目标检测的查准率达到95.81%,比Cascade R-CNN方法提高了11.57个百分点。展开更多
基金supported by the Program of Introducing Talents of Discipline to Universities(111 Plan)of China(B14010)the National Natural Science Foundation of China(31727901)
文摘While moving ahead with the object detection technology, especially deep neural networks, many related tasks, such as medical application and industrial automation, have achieved great success. However, the detection of objects with multiple aspect ratios and scales is still a key problem. This paper proposes a top-down and bottom-up feature pyramid network(TDBU-FPN),which combines multi-scale feature representation and anchor generation at multiple aspect ratios. First, in order to build the multi-scale feature map, this paper puts a number of fully convolutional layers after the backbone. Second, to link neighboring feature maps, top-down and bottom-up flows are adopted to introduce context information via top-down flow and supplement suboriginal information via bottom-up flow. The top-down flow refers to the deconvolution procedure, and the bottom-up flow refers to the pooling procedure. Third, the problem of adapting different object aspect ratios is tackled via many anchor shapes with different aspect ratios on each multi-scale feature map. The proposed method is evaluated on the pattern analysis, statistical modeling and computational learning visual object classes(PASCAL VOC)dataset and reaches an accuracy of 79%, which exhibits a 1.8% improvement with a detection speed of 23 fps.
文摘在钢铁行业中,碳化物是钢材中一种非常重要的组成成分,其在钢材中的分布对于评估钢材的质量具有很高的参考价值。然而,目前棒材碳化物的检测手段主要为人工检测,成本高昂且缺乏稳定性。引入人工智能领域的深度学习技术,收集并标注了3192张高质量钢铁棒材带状碳化物图像与11个完整样品数据,创建了工业场景下的棒材带状碳化物目标检测数据集(Banded carbide dataset on object detection for steel bar,BCDOD)。使用深度学习领域中常见的目标检测方法对数据集进行了实验分析,针对应用场景与数据的特点,引入旋转数据增强、Focal Loss函数与负样本微调对级联R⁃CNN模型进行改进,提升了模型的性能,平均精度达到96%。同时,在完整样品数据取得了100%的识别准确率,取得了较为理想的效果,弥补了人工智能技术在碳化物金相检测领域的空缺。
文摘针对光照不均、噪声大、拍摄质量不高的夜晚水下环境,为实现夜晚水下图像中鱼类目标的快速检测,利用计算机视觉技术,提出了一种基于改进Cascade R-CNN算法和具有色彩保护的MSRCP(Multi-scale Retinex with color restoration)图像增强算法的夜晚水下鱼类目标检测方法。首先针对夜晚水下环境的视频数据,根据时间间隔,截取出相应的夜晚水下鱼类图像,对截取的原始图像进行MSRCP图像增强。然后采用DetNASNet主干网络进行网络训练和水下鱼类特征信息的提取,将提取出的特征信息输入到Cascade R-CNN模型中,并使用Soft-NMS候选框优化算法对其中的RPN网络进行优化,最后对夜晚水下鱼类目标进行检测。实验结果表明,该方法解决了夜晚水下环境中的图像降质、鱼类目标重叠检测问题,实现了对夜晚水下鱼类目标的快速检测,对夜晚水下鱼类图像目标检测的查准率达到95.81%,比Cascade R-CNN方法提高了11.57个百分点。
