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Real-time defects detection for apple sorting using NIR cameras with pruning-based YOLOV4 network

文献类型： 外文期刊

作者： Fan, Shuxiang 1 ; Liang, Xiaoting 1 ; Huang, Wenqian 1 ; Zhang, Vincent Jialong 3 ; Pang, Qi 1 ; He, Xin 1 ; Li, Lianjie 1 ; Zhang, Chi 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

2.Natl Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

3.Univ Chicago Lab Sch, Chicago, IL 60637 USA

4.Shanghai Ocean Univ, Shanghai 201306, Peoples R China

关键词： Machine vision; Defective apples; Fruit sorting; Deep learning; Object detection

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：6.757； 五年影响因子：6.817 ）

ISSN： 0168-1699

年卷期： 2022 年 193 卷

页码：

收录情况： SCI

摘要： In order to realize on-line detection of defective apples on a two-lane fruit sorting machine, an inspection module was constructed using NIR cameras and a diffuse illumination chamber. A real-time apple defects inspection method was proposed based on YOLO V4 deep learning algorithm. The input images were generated by combining NIR images in three consecutive rubber roller stations. Channel pruning and layer pruning methods were used to simplify the YOLO V4 network and accelerate the detection speed. A non-maximum suppression (NMS) method based on Ll norm is proposed to remove redundant prediction box after fine-tuning the pruned network. The test results indicated that the model size and inference time of the pruning-based YOLO V4 network was decreased by 241.24 megabyte (MB) and 10.82 ms, respectively, and the mean average precision (mAP) was increased from 91.82% to 93.74%, compared with the YOLO V4 network before pruning. The pruning-based YOLO V4 network based on NIR images was not affected by the variation of skin color and suitable for detects identification of different cultivars including 'Fuji' apple covered in red-yellow striping and red blush, `Golden Delicious', and 'Granny Smith', with the average detection accuracy of 93.9% at the on-line test assessing five fruit per second. The overall results showed that the proposed pruning-based YOLO V4 network combined with the developed inspection module, had great potential to be implemented in commercial fruit packing line for fruit defects identification.

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