  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.
