Improvement of Yolov8 for detection of citrus fruit color-changing
In order to improve the recognition of citrus fruit in natural environment,aiming at the problem that fruit detection is difficult due to the large crown,small individual fruit,dense fruit and serious occlusion in the current citrus target detection,an I-Yolov8 detection model with small target detection layer added to the Yolov8 detection model was proposed based on the fruit dataset constructed by the unilateral complete crown image of Citrus unshiu Marc.cv.Miyagawa wase during mid-late stages of fruit color-changing period under natural conditions.The results showed that the canopy in the natural environment enriches the target features of the dataset,and the added small target detection layer could be used to detect the targets with or above 4 pixel×4 pixel,and the mean Average Precision(mAP)of the model could reach 93.5%,which was 1.3%higher than that of Yolov8.The detection accuracy of I-Yolov8 and Yolov8 was 100%,respectively in sunny and cloudy natural scenarios,and the recall rate of I-Yolov8 was 72.45%and 91.61%,respectively,which was 16.33 and 14.63 percent higher than that of Yolov8,respectively.Therefore,the I-Yolov8 network model has high detection accuracy for citrus fruit in the natural environment and high application potential.
Yolov8small target detection layerMiyagawa wasecanopy fruit image
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