Lightweight Detection Algorithm of Kiwifruit Based on Improved YOLOX-S

Junchi Zhou, Wenwu Hu, Airu Zou, Shike Zhai, Tianyu Liu, Wenhan Yang and Ping Jiang
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Junchi Zhou: College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410128, China
Wenwu Hu: College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410128, China
Airu Zou: College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410128, China
Shike Zhai: College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410128, China
Tianyu Liu: College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410128, China
Wenhan Yang: College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410128, China
Ping Jiang: College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410128, China

Agriculture, 2022, vol. 12, issue 7, 1-14

Abstract: Considering the high requirements of current kiwifruit picking recognition systems for mobile devices, including the small number of available features for image targets and small-scale aggregation, an enhanced YOLOX-S target detection algorithm for kiwifruit picking robots is proposed in this study. This involved designing a new multi-scale feature integration structure in which, with the aim of providing a small and lightweight model, the feature maps used for detecting large targets in the YOLOX model are eliminated, the feature map of small targets is sampled through the nearest neighbor values, the superficial features are spliced with the final features, the gradient of the SiLU activation function is perturbed, and the loss function at the output is optimized. The experimental results show that, compared with the original YOLOX-S, the enhanced model improved the detection average precision (AP) of kiwifruit images by 6.52%, reduced the number of model parameters by 44.8%, and improved the model detection speed by 63.9%. Hence, with its outstanding effectiveness and relatively light weight, the proposed model is capable of effectively providing data support for the 3D positioning and automated picking of kiwifruit. It may also successfully provide solutions in similar fields related to small target detection.

Keywords: YOLOX; small target scale; loss function; feature integration; fruit picking (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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