In order to further improve the speed and precision of multi-scale object detection， and to solve the situations such as miss detection， wrong detection and repeated detection caused by small object detection， an object detection algorithm based on improved You Only Look Once v3 （YOLOv3） was proposed to realize automatic detection of multi-scale object. Firstly， the network structure was improved in the feature extraction network， and the attention mechanism was introduced into the spatial dimensions of residual module to pay attention to small objects. Then， Dense Convulutional Network （DenseNet） was used to fully integrate shallow information of the network， and the depthwise separable convolution was used to replace the normal convolution of the backbone network， thereby reducing the number of model parameters and improving the detection speed. In the feature fusion network， the bidirectional fusion of the shallow and deep features was realized through the bidirectional feature pyramid structure， and the 3-scale prediction was changed to 4-scale prediction， which improved the learning ability of multi-scale features. In terms of loss function， Generalized Intersection over Union （GIoU） was selected as the loss function， so that the precision of identifying objects was increased， and the object miss rate was reduced. Experimental results show that on Pascal VOC datasets， the mean Average Precision （mAP） of the improved YOLOv3 algorithm is as high as 83.26%， which is 5.89 percentage points higher than that of the original YOLOv3 algorithm， and the detection speed of the improved algorithm reaches 22.0 frame/s. Compared with the original YOLOv3 algorithm on Common Objects in COntext （COCO） dataset， the improved algorithm has the mAP improved by 3.28 percentage points. At the same time， in multi-scale object detection， the mAP of the algorithm has been improved， which verifies the effectiveness of the object detection algorithm based on the improved YOLOv3.