A novel two-stage deep learning-based small-object detection using hyperspectral images

Lu, Yan; Yamawaki, Masahiro; Noro, Naoki; Takara, Yohei; Ando, Fuminori

doi:10.1007/s10043-019-00528-0

Cited by 15 publications

(5 citation statements)

References 23 publications

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“…HSI imagery captures data at different wavelengths, making them more information-rich than traditional RGB or multi-spectral images. The in-depth information represented by hyperspectral images can be helpful in detecting different textures, chemicals, inks, etc., at pixel level [ 15 ].…”

Section: Hyperspectral Images: Importance and Challengesmentioning

confidence: 99%

Analysis of Hyperspectral Data to Develop an Approach for Document Images

Zaman,

Ahmed,

Malik

2023

Sensors

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Hyperspectral data analysis is being utilized as an effective and compelling tool for image processing, providing unprecedented levels of information and insights for various applications. In this manuscript, we have compiled and presented a comprehensive overview of recent advances in hyperspectral data analysis that can provide assistance for the development of customized techniques for hyperspectral document images. We review the fundamental concepts of hyperspectral imaging, discuss various techniques for data acquisition, and examine state-of-the-art approaches to the preprocessing, feature extraction, and classification of hyperspectral data by taking into consideration the complexities of document images. We also explore the possibility of utilizing hyperspectral imaging for addressing critical challenges in document analysis, including document forgery, ink age estimation, and text extraction from degraded or damaged documents. Finally, we discuss the current limitations of hyperspectral imaging and identify future research directions in this rapidly evolving field. Our review provides a valuable resource for researchers and practitioners working on document image processing and highlights the potential of hyperspectral imaging for addressing complex challenges in this domain.

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Section: Hyperspectral Images: Importance and Challengesmentioning

confidence: 99%

Analysis of Hyperspectral Data to Develop an Approach for Document Images

Zaman,

Ahmed,

Malik

2023

Sensors

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“…They reduced the quantity of convolutional kernels of GoogLeNet and adjusted the structure of Inception, and these measures improved the accuracy of GoogLeNet by 2%. The above algorithms all belong to fundamental series of CNN, and state-of-the-art algorithms commonly utilize these models as the backbone network, and then perform different optimizations to form the one-stage [ 37 ] and two-stage detection methods [ 38 ]. The one-stage detection approaches include the SSD and YOLO series, which are used to perform uniform and dense sampling on the different positions of an image to generate bounding boxes of different scales and aspect ratios.…”

Section: Related Workmentioning

confidence: 99%

Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting

Liu

Wang

et al. 2022

Sensors

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Advancements in deep learning and computer vision have led to the discovery of numerous effective solutions to challenging problems in the field of agricultural automation. With the aim to improve the detection precision in the autonomous harvesting process of green asparagus, in this article, we proposed the DA-Mask RCNN model, which utilizes the depth information in the region proposal network. Firstly, the deep residual network and feature pyramid network were combined to form the backbone network. Secondly, the DA-Mask RCNN model added a depth filter to aid the softmax function in anchor classification. Afterwards, the region proposals were further processed by the detection head unit. The training and test images were mainly acquired from different regions in the basin of the Yangtze River. During the capturing process, various weather and illumination conditions were taken into account, including sunny weather, sunny but overshadowed conditions, cloudy weather, and daytime greenhouse conditions as well as nighttime greenhouse conditions. Performance experiments, comparison experiments, and ablation experiments were carried out using the five constructed datasets to verify the effectiveness of the proposed model. Precision, recall, and F1-score values were applied to evaluate the performances of different approaches. The overall experimental results demonstrate that the balance of the precision and speed of the proposed DA-Mask RCNN model outperform those of existing algorithms.

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“…To make the network easier to learn, the defects are divided into two categories, including lack of locking pin and rusted-on nut, and a detailed description of the pin-level defect is shown in Figure 1. [23] is the basic two-stage target detection algorithm [24,25], which has achieved good results in many target detection tasks. Fast R-CNN is the most basic network architecture to ensure the good scalability of the algorithm.…”

Section: Training Datamentioning

confidence: 99%

Application of Data-Driven Iterative Learning Algorithm in Transmission Line Defect Detection

Chen

Wang

Shen

et al. 2021

Scientific Programming

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Deep learning technology has received extensive consideration in recent years, and its application value in target detection is also increasing day by day. In order to accelerate the practical process of deep learning technology in electric transmission line defect detection, the current work used the improved Faster R-CNN algorithm to achieve data-driven iterative training and defect detection functions for typical transmission line defect targets. Based on Faster R-CNN, we proposed an improved network that combines deformable convolution and feature pyramid modules and combined it with a data-driven iterative learning algorithm; it achieves extremely automated and intelligent transmission line defect target detection, forming an intelligent closed-loop image processing. The experimental results show that the increase of the recognition of improved Faster R-CNN network combined with data-driven iterative learning algorithm for the pin defect target is 31.7% more than Faster R-CNN. In the future, the proposed method can quickly improve the accuracy of transmission line defect target detection in a small sample and save manpower. It also provides some theoretical guidance for the practical work of transmission line defect target detection.

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A novel two-stage deep learning-based small-object detection using hyperspectral images

Cited by 15 publications

References 23 publications

Analysis of Hyperspectral Data to Develop an Approach for Document Images

Analysis of Hyperspectral Data to Develop an Approach for Document Images

Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting

Application of Data-Driven Iterative Learning Algorithm in Transmission Line Defect Detection

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