Lightweight Object Detection: A Study Based on YOLOv7 Integrated with ShuffleNetv2 and Vision Transformer
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 10 of its 14 references that are in the map. First posted on arXiv 4 Mar 2024.
Builds on
10 of 14 references.
References · 10
- 2017Attention Is All You Needcited by 60,361
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
- 2018YOLOv3: An Incremental Improvementcited by 2,659
- 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
- 2016YOLO9000: Better, Faster, Strongercited by 1,431
- 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,200
- 2018ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Designcited by 1,136
- 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,071
- 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 982
- 2022YOLOv6: A Single-Stage Object Detection Framework for Industrial Applicationscited by 275
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Citing papers per year: 2025: 2.
2 papers.