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

Source: [CSL-YOLO: A New Lightweight Object Detection System for Edge Computing](<https://www.sota2.com/research/paper/68fa6a541e209131cea27690>)

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

Data updated: 2025-10-25T23:21:36.715Z

Authors: Yu-Ming Zhang, Chun-Chieh Lee, Jun-Wei Hsieh, Kuo-Chin Fan

Published: 2021-07-10T12:56:46.000Z

## 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.

## 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/2107.04829>)

[PDF](<https://arxiv.org/pdf/2107.04829.pdf>)