@Article{cmc.2024.054768,
AUTHOR = {Anitha Ramachandran, Sendhil Kumar K.S.},
TITLE = {Border Sensitive Knowledge Distillation for Rice Panicle Detection in UAV Images},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {81},
YEAR = {2024},
NUMBER = {1},
PAGES = {827--842},
URL = {http://www.techscience.com/cmc/v81n1/58319},
ISSN = {1546-2226},
ABSTRACT = {Research on panicle detection is one of the most important aspects of paddy phenotypic analysis. A phenotyping method that uses unmanned aerial vehicles can be an excellent alternative to field-based methods. Nevertheless, it entails many other challenges, including different illuminations, panicle sizes, shape distortions, partial occlusions, and complex backgrounds. Object detection algorithms are directly affected by these factors. This work proposes a model for detecting panicles called Border Sensitive Knowledge Distillation (BSKD). It is designed to prioritize the preservation of knowledge in border areas through the use of feature distillation. Our feature-based knowledge distillation method allows us to compress the model without sacrificing its effectiveness. An imitation mask is used to distinguish panicle-related foreground features from irrelevant background features. A significant improvement in Unmanned Aerial Vehicle (UAV) images is achieved when students imitate the teacher’s features. On the UAV rice imagery dataset, the proposed BSKD model shows superior performance with 76.3% mAP, 88.3% precision, 90.1% recall and 92.6% F1 score.},
DOI = {10.32604/cmc.2024.054768}
}