# CSL-YOLO: A New Lightweight Object Detection System for Edge Computing

**Authors:** Yu-Ming Zhang, Chun-Chieh Lee, Jun-Wei Hsieh, Kuo-Chin Fan  
**arXiv:** [2107.04829](https://arxiv.org/abs/2107.04829)  
**Date:** 2021-07-10  
**Summary page:** https://www.summarizepaper.com/fr/paper/2107.04829v1/

## Abstract

The development of lightweight object detectors is essential due to the limited computation resources. To reduce the computation cost, how to generate redundant features plays a significant role. This paper proposes a new lightweight Convolution method Cross-Stage Lightweight (CSL) Module, to generate redundant features from cheap operations. In the intermediate expansion stage, we replaced Pointwise Convolution with Depthwise Convolution to produce candidate features. The proposed CSL-Module can reduce the computation cost significantly. Experiments conducted at MS-COCO show that the proposed CSL-Module can approximate the fitting ability of Convolution-3x3. Finally, we use the module to construct a lightweight detector CSL-YOLO, achieving better detection performance with only 43% FLOPs and 52% parameters than Tiny-YOLOv4.