  Object detection, one of the three main tasks of computer vision, has been
used in various applications. The main process is to use deep neural networks
to extract the features of an image and then use the features to identify the
class and location of an object. Therefore, the main direction to improve the
accuracy of object detection tasks is to improve the neural network to extract
features better. In this paper, I propose a convolutional module with a
transformer[1], which aims to improve the recognition accuracy of the model by
fusing the detailed features extracted by CNN[2] with the global features
extracted by a transformer and significantly reduce the computational effort of
the transformer module by deflating the feature mAP. The main execution steps
are convolutional downsampling to reduce the feature map size, then
self-attention calculation and upsampling, and finally concatenation with the
initial input. In the experimental part, after splicing the block to the end of
YOLOv5n[3] and training 300 epochs on the coco dataset, the mAP improved by
1.7% compared with the previous YOLOv5n, and the mAP curve did not show any
saturation phenomenon, so there is still potential for improvement. After 100
rounds of training on the Pascal VOC dataset, the accuracy of the results
reached 81%, which is 4.6 better than the faster RCNN[4] using resnet101[5] as
the backbone, but the number of parameters is less than one-twentieth of it.
