Cited 4 time in
Cited 5 time in
Orchard Free Space and Center Line Estimation Using Naive Bayesian Classifier for Unmanned Ground Self-Driving Vehicle
- Orchard Free Space and Center Line Estimation Using Naive Bayesian Classifier for Unmanned Ground Self-Driving Vehicle
- Lyu, Hong-Kun; Park, Chi-Ho; Han Dong-Hee; Kwak, Seong Woo; Choi, Byeongdae
- DGIST Authors
- Lyu, Hong-Kun; Park, Chi-Ho; Choi, Byeongdae
- Issue Date
- Symmetry, 10(9), 1-14
- Article Type
- Autonomous navigation; Image processing; Machine vision; Orchard free space; Precision agriculture; Unmanned ground vehicle
- In the case of autonomous orchard navigation, researchers have developed algorithms that utilize features, such as trunks, canopies, and sky in orchards, but there are still various difficulties in recognizing free space for autonomous navigation in a changing agricultural environment. In this study, we applied the Naive Bayesian classification to detect the boundary between the trunk and the ground and propose an algorithm to determine the center line of free space. The naïve Bayesian classification requires a small number of samples for training and a simple training process. In addition, it was able to effectively classify tree trunk's points and noise points of the orchard, which are problematic in vision-based processing, and noise caused by small branches, soil, weeds, and tree shadows on the ground. The performance of the proposed algorithm was investigated using 229 sample images obtained from an image acquisition system with a Complementary Metal Oxide Semiconductor (CMOS) Image Sensor (CIS) camera. The center line detected by the unaided-eye manual decision and the results extracted by the proposed algorithm were compared and analyzed for several parameters. In all compared parameters, extracted center line was more stable than the manual center line results. © 2018 by the authors.
- Multidisciplinary Digital Publishing Institute
- Related Researcher
There are no files associated with this item.
- Intelligent Devices and Systems Research Group1. Journal Articles
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.