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.

本研究通过使用高级技术（如 Group Convolution、ShuffleNetV2 和 Vision Transformer），优化了 YOLOv7 算法，减小了模型的参数数量和内存使用，并简化了网络架构，从而在资源受限的设备上加强了实时物体检测的能力。实验结果表明，经过改进的 YOLO 模型表现出卓越的性能，在保持卓越的检测准确度的同时，显著提高了处理速度。