Open Access

ARTICLE

FMCSNet: Mobile Devices-Oriented Lightweight Multi-Scale Object Detection via Fast Multi-Scale Channel Shuffling Network Model

Lijuan Huang¹, Xianyi Liu², Jinping Liu^2,*, Pengfei Xu^2,*

1 Hunan Intelligent Rehabilitation Robot and Auxiliary Equipment Engineering Technology Research Center, Changsha, 410004, China
2 College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China

* Corresponding Authors: Jinping Liu. Email: ; Pengfei Xu. Email:

(This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)

Computers, Materials & Continua 2026, 86(1), 1-20. https://doi.org/10.32604/cmc.2025.068818

Received 06 June 2025; Accepted 03 September 2025; Issue published 10 November 2025

Abstract

The ubiquity of mobile devices has driven advancements in mobile object detection. However, challenges in multi-scale object detection in open, complex environments persist due to limited computational resources. Traditional approaches like network compression, quantization, and lightweight design often sacrifice accuracy or feature representation robustness. This article introduces the Fast Multi-scale Channel Shuffling Network (FMCSNet), a novel lightweight detection model optimized for mobile devices. FMCSNet integrates a fully convolutional Multilayer Perceptron (MLP) module, offering global perception without significantly increasing parameters, effectively bridging the gap between CNNs and Vision Transformers. FMCSNet achieves a delicate balance between computation and accuracy mainly by two key modules: the ShiftMLP module, including a shift operation and an MLP module, and a Partial group Convolutional (PGConv) module, reducing computation while enhancing information exchange between channels. With a computational complexity of 1.4G FLOPs and 1.3M parameters, FMCSNet outperforms CNN-based and DWConv-based ShuffleNetv2 by 1% and 4.5% mAP on the Pascal VOC 2007 dataset, respectively. Additionally, FMCSNet achieves a mAP of 30.0 (0.5:0.95 IoU threshold) with only 2.5G FLOPs and 2.0M parameters. It achieves 32 FPS on low-performance i5-series CPUs, meeting real-time detection requirements. The versatility of the PGConv module’s adaptability across scenarios further highlights FMCSNet as a promising solution for real-time mobile object detection.

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Keywords

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