Given the current limitations in terms of computational resources,detection accuracy,and speed of existing logistic parcel object detection models,a lightweight model called GSYOLO with low redundant features is proposed,which is used for fast detection of logistic parcels.Initially,it pro-poses a lightweight feature extraction module called GSBlock as the backbone network,which extracts representative features from input images while ensuring high accuracy.The model is further optimized by incorporating various lightweight modules and parameter-free attention mechanisms to significantly reduce the parameters and floating-point operations of the backbone network,thereby achieving fast inference and low power consumption.Comparative experiments using a self-built dataset of logistic parcels demonstrate that the GSYOLO model achieves a mAP of 98.6％,the model parameters have been reduced by 94.75％,with a reduction of in model parameters and a decrease 96％in FLOPs.The GSYOLO model significantly reduces parameters and FLOPs while achieving a higher detection accura-cy,making it particularly suitable for logistic parcel detection scenarios with limited computing re-sources.
维普
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