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.