Industrial Environment Flat Area Dust Recognition and Cleanliness Assessment Based on YOLOv8
To solve the problem of lack of dust accumulation recognition in the flat area cleaning of industrial scenes with high cleanliness requirements,the efficient YOLOv8 algorithm is used to identify the category and number of ash piles,grey spots and reflections on the flat area,and assign different weighting parameters to weight the cleanliness index,and then evaluate the cleanliness level of the detected area.The system directs the flat area cleaning robot to perform cleaning tasks according to the cleanliness level,so that it can complete the cleaning operation efficiently.The model achieves 80.6％mAP@0.5 on the test dataset,and the real-time camera detection frame rate can reach 50～143 frames/s,which can accurately perform flat area dust identification and cleanliness assessment of the detection area.
deep learningYOLOv8flat area dust accumulation recognitionflat area cleanliness assessment
万方数据
维普
