A Fast Neural Network Based on Attention Mechanisms for Detecting Field Flat Jujube

Shilin Li, Shujuan Zhang, Jianxin Xue, Haixia Sun and Rui Ren
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Shilin Li: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030800, China
Shujuan Zhang: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030800, China
Jianxin Xue: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030800, China
Haixia Sun: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030800, China
Rui Ren: College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030800, China

Agriculture, 2022, vol. 12, issue 5, 1-19

Abstract: The efficient identification of the field flat jujube is the first condition to realize its automated picking. Consequently, a lightweight algorithm of target identification based on improved YOLOv5 (you only look once) is proposed to meet the requirements of high-accuracy and low-complexity. At first, the proposed method solves the imbalance of data distribution by improving the methods of data enhancement. Then, to improve the accuracy of the model, we adjust the structure and the number of the Concentrated-Comprehensive Convolution Block modules in the backbone network, and introduce the attention mechanisms of Efficient Channel Attention and Coordinate Attention. On this basis, this paper makes lightweight operations by using the Deep Separable Convolution to reduce the complexity of the model. Ultimately, the Complete Intersection over Union loss function and the non-maximum suppression of Distance Intersection over Union are used to optimize the loss function and the post-processing process, respectively. The experimental results show that the mean average precision of improved network reaches 97.4%, which increases by 1.7% compared with the original YOLOv5s network; and, the parameters, floating point of operations, and model size are compressed to 35.39%, 51.27%, and 37.5% of the original network, respectively. The comparison experiments are conducted around the proposed method and the common You Only Look Once target detection algorithms. The experimental results show that the mean average precision of the proposed method is 97.4%, which is higher than the 90.7%, 91.7%, and 88.4% of the YOLOv3, YOLOv4, and YOLOx-s algorithms, and the model size decreased to 2.3%, 2.2%, and 15.7%, respectively. The improved algorithm realizes a reduction of complexity and an increase in accuracy, it can be suitable for lightweight deployment to a mobile terminal at a later stage, and it provides a certain reference for the visual detection of picking robots.

Keywords: target detection; identifying the field flat jujube; YOLOv5; convolutional neural network (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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