deepNIR: Dataset for generating synthetic NIR images and improved fruit detection system using deep learning techniques

This page introduces our paper "Dataset for generating synthetic NIR images and improved fruit detection system using deep learning techniques" and if you want to download dataset, please find them from Download.

Abstract

This paper presents datasets utilised for synthetic near-infrared (NIR) image generation and bounding-box level fruit detection systems. A high-quality dataset is one of the essential building blocks that can lead to success in model generalisation and the deployment of data-driven deep neural networks. In particular, synthetic data generation tasks often require more training samples than other supervised approaches.

Therefore, in this paper, we share the NIR+RGB datasets that are re-processed from two public datasets (i.e., nirscene and SEN12MS), expanded our previous study, deepFruits, and our novel NIR+RGB sweet pepper (capsicum) dataset. We oversampled from the original nirscene dataset at 10, 100, 200, and 400 ratios that yielded a total of 127k pairs of images. From the SEN12MS satellite multispectral dataset, we selected Summer (45k) and All seasons (180k) subsets and applied a simple yet important conversion: digital number (DN) to pixel value conversion followed by image standardisation. Our sweet pepper dataset consists of 1615 pairs of NIR+RGB images that were collected from commercial farms.

We quantitatively and qualitatively demonstrate that these NIR+RGB datasets are sufficient to be used for synthetic NIR image generation. We achieved Frechet inception distances (FIDs) of 11.36, 26.53, and 40.15 for nirscene1, SEN12MS, and sweet pepper datasets, respectively. In addition, we release manual annotations of 11 fruit bounding boxes that can be exported in various formats using cloud service. Four newly added fruits (blueberry, cherry, kiwi and wheat) compound 11 novel bounding box datasets on top of our previous work presented in the deepFruits project (apple, avocado, capsicum, mango, orange, rockmelon and strawberry). The total number of bounding box instances of the dataset is 162k and it is ready to use from a cloud service. For the evaluation of the dataset, Yolov5 single stage detector is exploited and reported impressive mean-average-precision, \(\text{mAP}_{[0.5:0.95]}\) results of [min:0.49, max:0.812]. We hope these datasets are useful and serve as a baseline for future studies.

Open access paper

You can our open access published paper MDPI sensors link.

Contact and reference

Please have a look that page and inform us if there is any unclear/ambiguous things to enddl22@gmail.com In addition, if this dataset is helpful for your research or application, please do consider to cite our paper as follow

@Article{s22134721,
AUTHOR = {Sa, Inkyu and Lim, Jong Yoon and Ahn, Ho Seok and MacDonald, Bruce},
TITLE = {deepNIR: Datasets for Generating Synthetic NIR Images and Improved Fruit Detection System Using Deep Learning Techniques},
JOURNAL = {Sensors},
VOLUME = {22},
YEAR = {2022},
NUMBER = {13},
ARTICLE-NUMBER = {4721},
URL = {https://www.mdpi.com/1424-8220/22/13/4721},
PubMedID = {35808218},
ISSN = {1424-8220},
DOI = {10.3390/s22134721}
}