Deep learning is currently the mainstream method of object detection. Faster region-based convolutional neural network (Faster R-CNN) has a pivotal position in deep learning. It has impressive detection effects in ordinary scenes. However, under special conditions, there can still be unsatisfactory detection performance, such as the object having problems like occlusion, deformation, or small size. This paper proposes a novel and improved algorithm based on the Faster R-CNN framework combined with the Faster R-CNN algorithm with skip pooling and fusion of contextual information. This algorithm can improve the detection performance under special conditions on the basis of Faster R-CNN. The improvement mainly has three parts: The first part adds a context information feature extraction model after the conv5_3 of the convolutional layer; the second part adds skip pooling so that the former can fully obtain the contextual information of the object, especially for situations where the object is occluded and deformed; and the third part replaces the region proposal network (RPN) with a more efficient guided anchor RPN (GA-RPN), which can maintain the recall rate while improving the detection performance. The latter can obtain more detailed information from different feature layers of the deep neural network algorithm, and is especially aimed at scenes with small objects. Compared with Faster R-CNN, you only look once series (such as: YOLOv3), single shot detector (such as: SSD512), and other object detection algorithms, the algorithm proposed in this paper has an average improvement of 6.857% on the mean average precision (mAP) evaluation index while maintaining a certain recall rate. This strongly proves that the proposed method has higher detection rate and detection efficiency in this case.

中文翻译：

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基于快速R-CNN算法的跳池合并上下文信息融合目标检测

深度学习是当前对象检测的主流方法。更快的基于区域的卷积神经网络（Faster R-CNN）在深度学习中具有举足轻重的地位。在普通场景中具有出色的检测效果。但是，在特殊条件下，检测性能仍然不能令人满意，例如物体具有诸如咬合，变形或尺寸小的问题。本文提出了一种基于Faster R-CNN框架并结合Faster R-CNN算法，跳过合并和上下文信息融合的改进算法。该算法可以在Faster R-CNN的基础上提高特殊条件下的检测性能。改进主要包括三个部分：第一部分在卷积层的conv5_3之后添加上下文信息特征提取模型；第二部分在卷积层的conv5_3之后添加上下文信息特征提取模型。第二部分增加了跳过池，以便前者可以完全获取对象的上下文信息，尤其是在对象被遮挡和变形的情况下；第三部分用更高效的导向锚RPN（GA-RPN）代替了区域提议网络（RPN），可以在提高检测性能的同时保持召回率。后者可以从深度神经网络算法的不同特征层获得更详细的信息，并且特别针对具有小对象的场景。与Faster R-CNN相比，您只需查看一次序列（例如：YOLOv3），单发检测器（例如：SSD512）和其他对象检测算法，本文提出的算法平均改进了6次。平均平均准确度（mAP）评估指数的85％，同时保持一定的召回率。这有力地证明了该方法在这种情况下具有较高的检测率和检测效率。