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Abstract An improved Y0L0v5n model for automatically detecting aggressive behaviors among sheep inscale and intensive farming was proposed. In the YOLOvSn lightweight architecture, the traditional con-volution module was displaced by the ghost convolution module to reduce the model's parameters andcomputing overhead. Subsequently, the coordinate attention (CA) mechanism was introduced to the keylocations on the network to enhance the channel and localization information. Additionally, an efficientintersection over union (ElOU) loss funetion was introduced and improved to increase the accuracy of thepredictive frame and reduce false detection. Finally, the stochastic gradient descent (SGD)optimizerwas replaced with Ranger2l to compensate for the poor performance of the ghost module in feature extrac-tion and hasten convergence. To improve the model's generalization capacity for all weather conditions,three weather conditions (snowy, rainy, and foggy) were added to half of the sample data. Experimentsrevealed that compared to the original YOL0v5n model, our revised model required only 83% of the param-eters. lts size was only 3 MB and boosted the accuracy by 0.7% ,recall by 1.3% , mean average precision(mAP)by 0.5% , and Fl by 1.01%. It also outperforms other popular lightweight YOL0 models.
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2023, Vol. 36 
