  The proposed YOLO-Former method seamlessly integrates the ideas of
transformer and YOLOv4 to create a highly accurate and efficient object
detection system. The method leverages the fast inference speed of YOLOv4 and
incorporates the advantages of the transformer architecture through the
integration of convolutional attention and transformer modules. The results
demonstrate the effectiveness of the proposed approach, with a mean average
precision (mAP) of 85.76\% on the Pascal VOC dataset, while maintaining high
prediction speed with a frame rate of 10.85 frames per second. The contribution
of this work lies in the demonstration of how the innovative combination of
these two state-of-the-art techniques can lead to further improvements in the
field of object detection.
