Detection of Residual Film on the Field Surface Based on Faster R-CNN Multiscale Feature Fusion

Tong Zhou, Yongxin Jiang, Xuenong Wang, Jianhua Xie (), Changyun Wang (), Qian Shi and Yi Zhang
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Tong Zhou: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Yongxin Jiang: Research Institute of Agricultural Mechanization, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, China
Xuenong Wang: Research Institute of Agricultural Mechanization, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, China
Jianhua Xie: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Qian Shi: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Yi Zhang: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China

Agriculture, 2023, vol. 13, issue 6, 1-13

Abstract: After the residual film recycling machine recovers the film, some small pieces of the film will remain on the surface of the field. To solve the problem of collecting small pieces of film, it is necessary to develop a piece of intelligent picking equipment. The detection of small pieces of film is the first problem to be solved. This study proposes a method of an object detection algorithm fusing multi-scale features (MFFM Faster R-CNN) based on improved Faster R-CNN. Based on the Faster R-CNN model, the feature pyramid network is added to solve the problem of multiscale change of residual film. The convolution block attention module is introduced to enhance the feature extraction ability of the model. The Soft-NMS algorithm is used instead of the NMS algorithm to improve the detection accuracy of the model in the RPN network. The experimental results show that the model is able to effectively detect surface residual film in complex environments, with an AP of 83.45%, F 1-score of 0.89, and average detection time of 248.36 ms. The model is compared with SSD and YOLOv5 under the same experimental environment and parameters, and it is found that the model not only ensures high-precision detection but also ensures real-time detection. This lays the theoretical foundation for the subsequent development of field surface residual film intelligent picking equipment.

Keywords: residual film; Faster R-CNN; multiscale features; attention module; intelligent picking equipment (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: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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