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

Source: [Lightweight Object Detection: A Study Based on YOLOv7 Integrated with ShuffleNetv2 and Vision Transformer](<https://www.sota2.com/research/paper/68fa72671e209131cea56441>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:25:11.364Z

Authors: Wenkai Gong

Published: 2024-03-04T05:29:32.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2403.01736>)

[PDF](<https://arxiv.org/pdf/2403.01736.pdf>)