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
舰船目标识别包含舰船的定位和舰船型号的细粒度级分类,不仅要实现通常的目标检测任务,还需要完成精确的型号分类。由于海洋背景复杂、舰船本身特征多变,目标检测算法应用于舰船型号细粒度级识别时会出现漏检和误检的问题,针对该问题提...舰船目标识别包含舰船的定位和舰船型号的细粒度级分类,不仅要实现通常的目标检测任务,还需要完成精确的型号分类。由于海洋背景复杂、舰船本身特征多变,目标检测算法应用于舰船型号细粒度级识别时会出现漏检和误检的问题,针对该问题提出基于注意力机制的特征增强架构(Feature enhancement architecture based on attention mechanism,FBAM)。该架构中包含两个改进模块:顶层特征增强模块(Top-level feature enhancement,TLFE),通过融合通道注意力和空间注意力,为舰船识别提供丰富的语义信息和位置信息;自适应ROI特征增强(Adaptive ROI feature enhancement,ROIFE),网络自适应组合多层次的ROI特征信息,增强舰船识别的细粒度级别特征,提高舰船识别的定位能力。该架构可较简单的插入FPN特征融合模块中。利用HRSC2016数据集对提出的FBAM进行实验验证,实验结果证明了基于注意力机制的特征增强架构可以明显增强网络对于特征信息的利用,提高舰船目标检测的精度,并且可以较简单的应用到多个网络模型中。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.
文摘舰船目标识别包含舰船的定位和舰船型号的细粒度级分类,不仅要实现通常的目标检测任务,还需要完成精确的型号分类。由于海洋背景复杂、舰船本身特征多变,目标检测算法应用于舰船型号细粒度级识别时会出现漏检和误检的问题,针对该问题提出基于注意力机制的特征增强架构(Feature enhancement architecture based on attention mechanism,FBAM)。该架构中包含两个改进模块:顶层特征增强模块(Top-level feature enhancement,TLFE),通过融合通道注意力和空间注意力,为舰船识别提供丰富的语义信息和位置信息;自适应ROI特征增强(Adaptive ROI feature enhancement,ROIFE),网络自适应组合多层次的ROI特征信息,增强舰船识别的细粒度级别特征,提高舰船识别的定位能力。该架构可较简单的插入FPN特征融合模块中。利用HRSC2016数据集对提出的FBAM进行实验验证,实验结果证明了基于注意力机制的特征增强架构可以明显增强网络对于特征信息的利用,提高舰船目标检测的精度,并且可以较简单的应用到多个网络模型中。
