基于改进YOLOv3的目标识别方法

在复杂的自然场景中,目标识别存在背景干扰、周围物体遮挡和光照变化等问题,同时识别的目标大多拥有多种不同的尺寸和类型.针对上述目标识别存在的问题,本文提出了一种基于改进YOLOv3的非限制自然场景中中等或较大尺寸的目标识别方法 (简称CDSP-YOLO).该方法采用CLAHE图像增强预处理方法来消除自然场景中光照变化对目标识别效果的影响,并使用随机空间采样池化(S3Pool)作为特征提取网络的下采样方法来保留特征图的空间信息解决复杂环境中的背景干扰问题,而且对多尺度识别进行改进来解决YOLOv3对于中等或较大尺寸目标识别效果不佳的问题.实验结果表明:本文提出的方法在移动通信铁塔测试集上的准确率达97%,召回率达80%.与YOLOv3相比,该方法在非限制自然场景中的目标识别应用上具有更好的性能和推广应用前景.

Object Recognition Method Based on Improved YOLOv3

In complex natural scenes, object recognition encounters the problems such as background interference, occlusion of surrounding objects, and illumination changes. At the same time, most of the identified objects have different sizes and types. In view of the above-mentioned problem of object recognition, this study proposes a medium or large size object recognition method based on improved YOLOv3 in unrestricted natural scenes (CDSP-YOLO). This method uses CLAHE image enhancement preprocessing method to eliminate the influence of illumination changes on object recognition in natural scenes, and uses stochastic spatial sampling pooling (S3Pool) as the downsampling method of feature extraction network to preserve the spatial information of feature map to solve the background interference problem in complex environment, and improves multi-scale recognition to solve the problem that YOLOv3 is not effective for medium or large size object recognition. The experimental results show that the proposed method has an accuracy rate of 97% and a recall rate of 80% on the mobile communication tower test set. Compared with YOLOv3, the algorithm has better performance and application prospects in object recognition applications in unrestricted natural scenes.