A Novel Lightweight Grape Detection Method

Shuzhi Su (), Runbin Chen, Xianjin Fang, Yanmin Zhu, Tian Zhang and Zengbao Xu
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Shuzhi Su: School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan 232001, China
Runbin Chen: School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan 232001, China
Xianjin Fang: School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan 232001, China
Yanmin Zhu: School of Computer Mechanical Engineering, Anhui University of Science & Technology, Huainan 232001, China
Tian Zhang: School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan 232001, China
Zengbao Xu: School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan 232001, China

Agriculture, 2022, vol. 12, issue 9, 1-17

Abstract: This study proposes a novel lightweight grape detection method. First, the backbone network of our method is Uniformer, which captures long-range dependencies and further improves the feature extraction capability. Then, a Bi-directional Path Aggregation Network (BiPANet) is presented to fuse low-resolution feature maps with strong semantic information and high-resolution feature maps with detailed information. BiPANet is constructed by introducing a novel cross-layer feature enhancement strategy into the Path Aggregation Network, which fuses more feature information with a significant reduction in the number of parameters and computational complexity. To improve the localization accuracy of the optimal bounding boxes, a Reposition Non-Maximum Suppression (R-NMS) algorithm is further proposed in post-processing. The algorithm performs repositioning operations on the optimal bounding boxes by using the position information of the bounding boxes around the optimal bounding boxes. Experiments on the WGISD show that our method achieves 87.7% mAP, 88.6% precision, 78.3% recall, 83.1% F1 score, and 46 FPS. Compared with YOLOx, YOLOv4, YOLOv3, Faster R-CNN, SSD, and RetinaNet, the mAP of our method is increased by 0.8%, 1.7%, 3.5%, 21.4%, 2.5%, and 13.3%, respectively, and the FPS of our method is increased by 2, 8, 2, 26, 0, and 10, respectively. Similar conclusions can be obtained on another grape dataset. Encouraging experimental results show that our method can achieve better performance than other recognized detection methods in the grape detection tasks.

Keywords: grape detection; convolutional neural network; self-attention; deep learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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