Research on group pig target detection with improve YOLOv8n algorithm
In order to meet the needs of for model lightweight and high-precision detection for embedded devices,this paper proposed a group-raising pig target detection algorithm based on the improved YOLOv8n model.First,the C2fFB structure was introduced in the backbone network to reduce the amount of memory access and redundant calculations.Then the new Neck network was constructed with the BiFPN structure and the C2fSC module was introduced to further achieve deeper feature fusion and reduce of the spatial redundancy and channel redundancy of the fusion.Finally,SIoU was used to replace the original CIoU to improve the accuracy of the model.The experimental results showed that the F1 score,precision,recall rate,and average precision of the proposed algorithm were improved compared with the original algorithm by 3%,1.8%,3.5%,and 1.5%,respectively.And the number of parameters,calculation amount,and model size were reduced by 46.84%,27.16%,and 44.71%respectively.Therefore,the algorithm model in this paper provides an efficient target detection solution for the intelligent breeding of group-raising pigs.
object detectionYOLOv8ngroup pigsBiFPNintersection ratio
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