Department of Mechanical Engineering, College of Field Engineering, Army Engineering University of PLA, Nanjing 210007, China.
Sensors (Basel). 2020 Sep 25;20(19):5490. doi: 10.3390/s20195490.
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.
深度学习是目前物体检测的主流方法。快速区域卷积神经网络（Faster R-CNN）在深度学习中占有重要地位。它在普通场景下具有令人印象深刻的检测效果。然而，在特殊情况下，仍然可能存在检测性能不理想的情况，例如物体存在遮挡、变形或尺寸较小等问题。
本文提出了一种基于 Faster R-CNN 框架的改进算法，该算法结合了具有跳层池化和上下文信息融合的 Faster R-CNN 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。改进主要有三个部分：第一部分在卷积层的 conv5_3 后添加了一个上下文信息特征提取模型；第二部分添加了跳层池化，使前者能够充分获取物体的上下文信息，特别是对于物体被遮挡和变形的情况；第三部分用更高效的引导锚点 RPN（GA-RPN）替换了区域建议网络（RPN），可以在保持召回率的同时提高检测性能。后者可以从深度神经网络算法的不同特征层获取更详细的信息，特别针对小物体的场景。
与 Faster R-CNN、只看一次系列（如：YOLOv3）、单射检测器（如：SSD512）等物体检测算法相比，本文提出的算法在保持一定召回率的情况下，在平均精度（mAP）评估指标上平均提高了 6.857%。这有力地证明了该方法在这种情况下具有更高的检测率和检测效率。
