基于改进YOLO v4模型的马铃薯中土块石块检测方法

Detection Method of Clods and Stones from Impurified Potatoes Based on Improved YOLO v4 Algorithm

为实现收获后含杂马铃薯中土块石块的快速检测和剔除,提出了一种基于改进YOLO v4模型的马铃薯中土块石块检测方法。YOLO v4模型以CSPDarknet53为主干特征提取网络,在保证检测准确率的前提下,利用通道剪枝算法对模型进行剪枝处理,以简化模型结构、降低运算量。采用Mosaic数据增强方法扩充图像数据集(8621幅图像),对模型进行微调,实现了马铃薯中土块石块的检测。测试表明,剪枝后模型总参数量减少了94.37%,模型存储空间下降了187.35 MB,前向运算时间缩短了0.02 s,平均精度均值(Mean average precision,mAP)下降了2.1个百分点,说明剪枝处理可提升模型性能。为验证模型的有效性,将本文模型与5种深度学习算法进行比较,结果表明,本文算法mAP为96.42%,比Faster R-CNN、Tiny-YOLO v2、YOLO v3、SSD分别提高了11.2、11.5、5.65、10.78个百分点,比YOLO v4算法降低了0.04个百分点,模型存储空间为20.75 MB,检测速度为78.49 f/s,满足实际生产需要。

A method based on improved YOLO v4 algorithm was proposed to realize the rapid detection of clods and stones from impurified potatoes after harvest.The YOLO v4 detection model was built on CSPDarknet53 framework.The channel pruning algorithm was used to prune the model to simplify the structure and the computational cost,while under the premise of detection accuracy.Mosaic data enhancement method was used to expand the image data set(8621 images),and the model was fine-tuned to achieve the detection of clods and stones from impurified potatoes.The test results showed that when the pruning rate was 0.8,the number of parameters of the model was reduced by 94.37%,the model size was decreased by 187.35 MB,the inference time was reduced by 24.1%,and the floating-point operations per second was compressed by 54.03%.It was shown that the performance of model can be improved by pruning.In order to verify the performance of the model,the model was compared with Faster R-CNN,Tiny-YOLO v2,YOLO v3,SSD and YOLO v4.The results showed that the mean average precision(mAP)of the model was 96.42%,the detection speed was 78.49 f/s,and the model size was 20.75 MB.The mean average precision was 11.2,11.5,5.65 and 10.78 percentage points higher than that of the other four algorithms and 2.1 percentage point lower than that of the YOLO v4 algorithm.The detection speed met the practical needs,and it can be applied to post-harvest potato impurity removal.