Improved Cherry Detection Method at Night Based on YOLOv7: YOLOv7-Cherry

被引：0

作者：

Gai, Rongli ^{[1
]}

Kong, Xiangzhou ^{[1
]}

Qin, Shan ^{[2
]}

Wei, Kai ^{[1
]}

机构：

[1] College of Information Engineering, Dalian University, Liaoning, Dalian

[2] Dalian Modern Agricultural Production Development Service Center, Liaoning, Dalian

来源：

Computer Engineering and Applications | 2024年 / 60卷 / 21期

关键词：

cherry recognition at night; image fusion; object detection; small target; YOLOv7;

D O I：

10.3778/j.issn.1002-8331.2306-0344

中图分类号：

学科分类号：

摘要：

To solve the problem that the cherry detection algorithm can not recognize the maturity of cherries in the night environment, an improved YOLOv7 algorithm: YOLOv7-Cherry is proposed. An image preprocessing method is used to combine the nighttime cherry image with the daytime cherry image in the same position to preserve the high spatial resolution information of the nighttime cherry image and enhance its spectral resolution. In YOLOv7-Cherry, the CBAM attention is firstly inserted into the backbone, and the attention mechanism is used to strengthen the representational ability of the neural network, emphasize important features, ignore secondary features, and enhance the extraction of cherry target features. Secondly, the recognition of small cherries are enhanced in images by the target detection algorithm, the small target detection layer is added. Then the initial detection box size of the original network is improved. Finally, to reduce the loss of cherry targets caused by the occlusion, the Soft-NMS method is used for the redundancy removal of the detection box. The experimental results demonstrate that YOLOv7-Cherry can significantly detect mature and immature cherries in night conditions. Compared with the YOLOv3, Faster- RCNN, YOLOv4, YOLOv5 and original YOLOv7 models, mAP of YOLOv7- Cherry model increased by 26.88, 25.05, 22.51, 17.11 and 7.66 percentage points, among which the precision, recall, mAP and F1 are 93.9%, 94.7%, 97.4% and 94.3%. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.

引用

页码：315 / 323

页数：8

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