Back to Search Start Over

Research on Apple Object Detection and Localization Method Based on Improved YOLOX and RGB-D Images

Authors :
Wang, Tiantian Hu
Wenbo Wang
Jinan Gu
Zilin Xia
Jian Zhang
Source :
Agronomy; Volume 13; Issue 7; Pages: 1816
Publication Year :
2023
Publisher :
Multidisciplinary Digital Publishing Institute, 2023.

Abstract

The vision-based fruit recognition and localization system is the basis for the automatic operation of agricultural harvesting robots. Existing detection models are often constrained by high complexity and slow inference speed, which do not meet the real-time requirements of harvesting robots. Here, a method for apple object detection and localization is proposed to address the above problems. First, an improved YOLOX network is designed to detect the target region, with a multi-branch topology in the training phase and a single-branch structure in the inference phase. The spatial pyramid pooling layer (SPP) with serial structure is used to expand the receptive field of the backbone network and ensure a fixed output. Second, the RGB-D camera is used to obtain the aligned depth image and to calculate the depth value of the desired point. Finally, the three-dimensional coordinates of apple-picking points are obtained by combining two-dimensional coordinates in the RGB image and depth value. Experimental results show that the proposed method has high accuracy and real-time performance: F1 is 93%, mean average precision (mAP) is 94.09%, detection speed can reach 167.43 F/s, and the positioning errors in X, Y, and Z directions are less than 7 mm, 7 mm, and 5 mm, respectively.

Details

Language :
English
ISSN :
20734395
Database :
OpenAIRE
Journal :
Agronomy; Volume 13; Issue 7; Pages: 1816
Accession number :
edsair.multidiscipl..76396f380257c7680c9728122d02463b
Full Text :
https://doi.org/10.3390/agronomy13071816