基于改進(jìn)YOLOv5s的蘋果表面缺陷檢測

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中圖分類號(hào)：S126：TP391.4 文獻(xiàn)標(biāo)識(shí)號(hào)：A 文章編號(hào)：1001-4942（2025）06-0149-09

AbstractAiming at the problems of low detection accuracy and miss and false detection caused by overlapping or obscured apples in apple surface defects detection，an improved apple surface defect detection method with YOLOv5s algorithm was proposed in this study.Firstly，the convolutional block atention module （CBAM）was added to the Backbone part of the YOLOv5s model to enhance the detection model's attention to the information of important regions of images，so as to improve the model's abilityto detect defects on the surface of apple. Secondly，a weighted bidirectional feature pyramid network（BiFPN）was introduced to fully integrate the apple surface defect features at diferent scales in orderto reduce missed and false detections.Finally，the Soft-NMS algorithm was used instead of the NMS algorithm in the original network to optimize the redundant bounding box screening conditions and further reduce the miss detection rate of the model.The experimental results showed that the proposed algorithm in this paper achieved 95.5% of mean average precision （mAP），which improved by 3.3 percentage point compared to the original algorithm，and the recall rate was improved by 4.6 percentage points，so it could be beter used to detect the surface defects of apples.

KeywordsApple surface defect detection； YOLOv5s； Convolutional atention mechanism；Weightedbidirectional feature pyramid network（BiFPN）

蘋果生長、采摘、運(yùn)輸以及篩選過程中常會(huì)遭遇鳥啄食、病蟲害以及磕碰、腐爛等情況，嚴(yán)重影響蘋果品質(zhì)、產(chǎn)量及經(jīng)濟(jì)價(jià)值，因此對蘋果表面缺陷進(jìn)行精準(zhǔn)檢測有著重要的意義。（剩余10014字）