A pedestrian‐target detection method based on multiscale convolution features is proposed to solve the problem of the existing target detection algorithms based on convolutional neural networks that cannot effectively adapt to the target scale change, its deformation, and the complex environment. This article analyzes the characteristics of small target, establishes the a three‐layer pyramid network structure based on horizontal connection fusion, and makes full use of shallow convolution features with lower semantics to improve the accuracy of small target detection; From the angle of the cross ratio, quantitative analysis is made on the effect of eliminating redundant bounding box by nonmaximum suppression based on center point when the window with the cross ratio is less than 0.7, solves the problem of missing detection of overlap and small target, and improves the target detection accuracy and the generalization ability of the target detection model. Through the experimental results on VOC2007 and VOC2012 public data sets, the proposed method of this article has higher detection accuracy and stability for small and incomplete targets, multiscale target detection, and target classification under the complex environment.

中文翻译：

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一种基于多尺度特征融合的改进目标检测方法

提出了一种基于多尺度卷积特征的行人目标检测方法，以解决现有的基于卷积神经网络的目标检测算法无法有效适应目标尺度变化，变形和复杂环境的问题。本文分析了小目标的特征，建立了基于水平连接融合的三层金字塔网络结构，充分利用了语义较浅的浅卷积特征，提高了小目标的检测精度。从交叉比率的角度出发，对交叉比率小于0.7的窗口进行基于中心点的非最大抑制消除冗余边界框的效果的定量分析，解决了重叠小目标检测遗漏的问题，提高了目标检测精度和目标检测模型的泛化能力。通过对VOC2007和VOC2012公共数据集的实验结果，本文提出的方法对小目标和不完整目标，多尺度目标检测以及复杂环境下的目标分类具有更高的检测精度和稳定性。