# Lightweight Object Detection: A Study Based on YOLOv7 Integrated with ShuffleNetv2 and Vision Transformer

arXiv: https://arxiv.org/abs/2403.01736 · HTML: https://mlsanity.com/paper/2403.01736/

## 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 (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://mlsanity.com/paper/2209.02976/) (2022)

## Built on by (most cited first, 2 of 2)

- [Performance Optimization of YOLO-FEDER FusionNet for Robust Drone Detection in Visually Complex Environments](https://mlsanity.com/paper/2509.14012/) (2025)
- [WD-DETR: Wavelet Denoising-Enhanced Real-Time Object Detection Transformer for Robot Perception with Event Cameras](https://mlsanity.com/paper/2506.09098/) (2025)

## 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.