Research on Appearance Detection Algorithm of Conveyor Line Noodle Prefabricated Dishes based on Improved YOLOv8
In response to the challenges encountered in the visual quality inspection of frozen dumpling pre-processed foods,such as the difficulty for the human eye to discern minor stuffing leaks,the indistinct color characteristics of the leaks,and the similarity between normal folds and leaks leading to frequent misjudgments and omissions in manual inspections,a novel appearance quality detection algorithm for conveyor line quality inspection in the pre-processed food industry,named"CL-YOLO",based on an improved yolov8,was proposed.In the backbone stage of this algorithm,two new interconnected convolutional attention modules were introduced.These modules,while ensuring model lightness,enhanced the weight distribution and learning across various dimensions of dumpling features,thereby improving the extraction of convolutional information specific to dumpling stuffing leaks.Prior to entering the neck network,a triplet attention module further enabled the feature map to stack more effectively with the neck network,intensifying the learning of dumpling characteristics.Experimental results demonstrated that compared to YOLOv8n,CL-YOLO achieved a 0.9% increase in mAP accuracy and a 1.8% increase in recall rate in the conveyor line dumpling recognition task.Moreover,with only 2,830,839 parameters,the algorithm reached a detection speed of 159FPS,meeting the deployment requirements for edge computing.In the EUM-DET dataset,an improvement of 1.8% in mAP accuracy and 0.5% in recall rate was observed,validating the effectiveness of the proposed structure.The structure presented in this research offers a new perspective for quality inspection in food conveyor line scenarios.
国家科技期刊平台
NSTL
万方数据
