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A real-time object detection model for orchard pests based on improved YOLOv4 algorithm

文献类型： 外文期刊

作者： Pang, Haitong 1 ; Zhang, Yitao 1 ; Cai, Weiming 1 ; Li, Bin 2 ; Song, Ruiyin 1 ;

作者机构： 1.NingboTech Univ, Sch Informat Sci & Engn, Ningbo 315100, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

期刊名称：SCIENTIFIC REPORTS （ 影响因子：4.996； 五年影响因子：5.516 ）

ISSN： 2045-2322

年卷期： 2022 年 12 卷 1 期

页码：

收录情况： SCI

摘要： Accurate and efficient real-time detection of orchard pests was essential and could improve the economic benefits of the fruit industry. The orchard pest dataset, PestImgData, was built through a series of methods such as web crawler, specimen image collection and data augmentation. PestImgData was composed of two parts, PestImgData-1 and PestImgData-2. It contained 24,796 color images and covered 7 types of orchard pests. Based on the PestImgData and YOLOv4 algorithm, this paper conducted a preliminary study on the real-time object detection of orchard pests from 4 perspectives: transfer learning, activation function, anchor box, and batch normalization. In addition, this paper also visualized the feature learning ability of the detection models. On the basis of the above research, three improvement measures were adopted: the post-processing NMS algorithm was upgraded to DIoU-NMS, the training method was upgraded to 2-time finetuning training and the training data was enhanced. The performance of the improved model, F-D-YOLOv4-PEST, had been effectively improved. The mean average precision of F-D-YOLOv4-PEST was 92.86%, and the detection time of a single picture was 12.22 ms, which could meet the real-time detection requirements. In addition, in the case of high overlap area or high density, F-D-YOLOv4-PEST still maintained good performance. In the testing process of the laboratory and the greenhouse, including the wired network and the wireless network, F-D-YOLOv4-PEST could locate and classify pests as expected. This research could provide technical reference for the intelligent identification of agricultural pests based on deep learning.

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