食品科学2026，Vol.47Issue(9)：63-74,12.DOI:10.7506/spkx1002-6630-20251105-024
基于轻量化SCFL-YOLO模型的鸡爪去骨部位图像分割技术
Image Segmentation Technique for Chicken Feet Deboning Areas Based on the Lightweight SCFL-YOLO Model
摘要
Abstract
To achieve real-time detection of deboning locations during intelligent chicken feet deboning,this study addresses challenges such as poor segmentation and detection due to the curved morphology of deboning areas and the similarity between edge features and the background,as well as the high computational load and parameter count of existing models that hinder real-time detection on edge computing devices with limited capabilities.We proposed an improved lightweight segmentation and detection model based on YOLOv11n-seg,SCFL-YOLO.First,a fused lightweight StarNet architecture was adopted to improve the efficiency.Second,the DynamicTanh activation function was introduced into C2PSA to enhance feature representation.Third,in the neck network,the original convolution in C3K2 was replaced by a new fused convolution(FPConv)to construct the FPC3K2 module,thereby reducing model complexity while strengthening multi-scale edge feature extraction.Finally,a novel lightweight shared convolutional prediction head,LSCPLQS,was designed to enable multi-scale segmentation and detection of different deboning regions of chicken feet.Experimental results showed that SCFL-YOLO achieved a detection accuracy of 97.5%,a segmentation accuracy of 94.8%,and an inference speed of 180.8 fps.Compared with the baseline model,the parameter count,computational cost,and model memory footprint of SCFL-YOLO were reduced by 40.3%,28.4%,and 39.7%,respectively,enabling effective segmentation and recognition of the claw pad and tarsometatarsal bone.Overall,SCFL-YOLO reduces computational complexity while maintaining high segmentation and detection performance,which can simplify the chicken feet deboning process and provide robust visual support for intelligent deboning equipment.关键词
鸡爪去骨/检测与分割/轻量化/自动化/深度学习Key words
chicken feet deboning/detection and segmentation/lightweight/automation/deep learning分类
轻工纺织引用本文复制引用
赵煜,陈鑫..基于轻量化SCFL-YOLO模型的鸡爪去骨部位图像分割技术[J].食品科学,2026,47(9):63-74,12.基金项目
上海市高校新进教师培训及科研启动项目(A1-2035-14-0010-31) （A1-2035-14-0010-31）
工业机器人在食品加工装备中的创新设计技术咨询项目(D-8006-25-0004) （D-8006-25-0004）
