Aiming at the problem of missed detection and wrong detection in the process of SSD(Single Shot Multibox Detector)multiscale object detection, this paper proposes a object detection algorithm that incorporates multidimensional dilated convolution and multiscale feature fusion. Among the multiscale features output by the convolutional neural network, the shallow layer has more detailed information, and the deep layer has more semantic information. According to this feature, this paper adopt three types of multidimensional dilated convolution shallow layers for the shallow network. The feature enhancement module obtains a feature map with semantic information, downsamples the enhanced feature map, and fusion features of different layers; at the same time, a channel attention module is introduced in the deep network to assign weights to channels, suppress useless information, and improve object The detection performance. The research results show that the detection accuracy of the algorithm in this paper is 79.7% on the PASCAL VOC dataset, which is 2.4% higher than the SSD algorithm; the detection accuracy on the KITTI dataset is 68.5%, which is 5.1% higher than the SSD algorithm, and the detection speed reaches realtime requirements have effectively improved the missed and wrongly detected object.