Potato malformation recognition based on improved YOLOv3 algorithm of feature extraction
Potato malformation seriously affects its commodity value,and the elimination of potato malformation has become one of the core work steps in the post production and pre-sale process of potatoes.At present,this process mainly relies on manual visual inspection,with large labor consumption and high investment cost.Therefore,accurate and efficient automatic detection technology needs to be developed urgently.In recent years,since machine vision has attracted extensive attention in the field of object appearance and feature recognition,and potato malformation belongs to potato morphological features,improved YOLOv3 algorithm was used to recognize potato malformation on the basis of obtaining potato appearance photos.By replacing the feature pyramid in the YOLOv3 algorithm with the feature pyramid of attention,the interference phenomenon in the process of feature fusion was overcome,the deep feature extraction of the network was enhanced,and the feature expression was optimized,so as to improve the accuracy and reliability of deformity detection.The experimental results showed that the accuracy of the improved YOLOv3 algorithm was improved by 2.68%compared with that before the improvement,F1 accuracy was 2.31%higher,mAP was 3.34%higher,and the detection ability for deep features was significantly enhanced.The algorithm is efficient and accurate,providing a better intelligent detection method for potato malformation detection.
NETL
NSTL
万方数据
维普
