Using a Bunch Testing Time Augmentations to Detect Rice Plants Based on Aerial Photography
-
Published:2024-02-02
Issue:3
Volume:13
Page:632
-
ISSN:2079-9292
-
Container-title:Electronics
-
language:en
-
Short-container-title:Electronics
Author:
Zhang Yu-Ming1ORCID, Chuang Chi-Hung2, Lee Chun-Chieh1, Fan Kuo-Chin1
Affiliation:
1. Department of Computer Science and Information Engineering, National Central University, Taoyuan 320, Taiwan 2. Department of Computer Science and Information Engineering, Chung Yuan Christian University, Taoyuan 320, Taiwan
Abstract
Crop monitoring focuses on detecting and identifying numerous crops within a limited region. A major challenge arises from the fact that the target crops are typically smaller in size compared to the image resolution, as seen in the case of rice plants. For instance, a rice plant may only span a few dozen pixels in an aerial image that comprises thousands to millions of pixels. This size discrepancy hinders the performance of standard detection methods. To overcome this challenge, our proposed solution includes a testing time grid cropping method to reduce the scale gap between rice plants and aerial images, a multi-scale prediction method for improved detection using cropped images based on varying scales, and a mean-NMS to prevent the potential exclusion of promising detected objects during the NMS stage. Furthermore, we introduce an efficient object detector, the Enhanced CSL-YOLO, to expedite the detection process. In a comparative analysis with two advanced models based on the public test set of the AI CUP 2021, our method demonstrated superior performance, achieving notable 4.6% and 2.2% increases in F1 score, showcasing impressive results.
Funder
National Science and Technology Council
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference39 articles.
1. Image segmentation for fruit detection and yield estimation in apple orchards;Bargoti;J. Field Robot.,2017 2. Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., and McCool, C. (2016). Deepfruits: A fruit detection system using deep neural networks. Sensors, 16. 3. Monocular camera based fruit counting and mapping with semantic data association;Liu;IEEE Robot. Autom. Lett.,2019 4. Mixtures of lightweight deep convolutional neural networks: Applied to agricultural robotics;McCool;IEEE Robot. Autom. Lett.,2017 5. Mortensen, A.K., Dyrmann, M., Karstoft, H., Jørgensen, R.N., and Gislum, R. (2016, January 26–29). Semantic segmentation of mixed crops using deep convolutional neural network. Proceedings of the CIGR-AgEng Conference, Aarhus, Denmark.
|
|