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.
@article{arxiv.2107.04829,
title = {CSL-YOLO: A New Lightweight Object Detection System for Edge Computing},
author = {Yu-Ming Zhang and Chun-Chieh Lee and Jun-Wei Hsieh and Kuo-Chin Fan},
journal= {arXiv preprint arXiv:2107.04829},
year = {2021}
}