  As mobile computing technology rapidly evolves, deploying efficient object
detection algorithms on mobile devices emerges as a pivotal research area in
computer vision. This study zeroes in on optimizing the YOLOv7 algorithm to
boost its operational efficiency and speed on mobile platforms while ensuring
high accuracy. Leveraging a synergy of advanced techniques such as Group
Convolution, ShuffleNetV2, and Vision Transformer, this research has
effectively minimized the model's parameter count and memory usage, streamlined
the network architecture, and fortified the real-time object detection
proficiency on resource-constrained devices. The experimental outcomes reveal
that the refined YOLO model demonstrates exceptional performance, markedly
enhancing processing velocity while sustaining superior detection accuracy.
