Toward Robotic Weed Control: Detection of Nutsedge Weed in Bermudagrass Turf Using Inaccurate and Insufficient Training Data
نویسندگان
چکیده
To enable robotic weed control, we develop algorithms to detect nutsedge from bermudagrass turf. Due the similarity between and background turf, manual data labeling is expensive error-prone. Consequently, directly applying deep learning methods for object detection cannot generate satisfactory results. Building on an instance approach (i.e. Mask R-CNN), combine synthetic with raw train network. We propose algorithm high fidelity data, adopting different levels of annotations reduce cost. Moreover, construct a skeleton-based probabilistic map (NSPM) as neural network input reliance pixel-wise precise labeling. also modify loss function cross entropy Kullback-Leibler divergence which accommodates uncertainty in process. implement proposed compare it both Faster R-CNN R-CNN. The results show that our design can effectively overcome impact imprecise insufficient training sample issues significantly outperform counterpart false negative rate only 0.4%. In particular, reduces time by 95% while achieving better performance if comparing original approach.
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ژورنال
عنوان ژورنال: IEEE robotics and automation letters
سال: 2021
ISSN: ['2377-3766']
DOI: https://doi.org/10.1109/lra.2021.3098012