MLP-based Efficient Stitching Method for UAV Images

It is important to obtain accurate information about kiwifruit vines to monitoring their physiological states and undertake precise orchard operations. However, because vines are small and cling to trellises, and have branches laying on the ground, numerous challenges exist in the acquisition of accurate data for kiwifruit vines. In this paper, a kiwifruit canopy distribution prediction model is proposed on the basis of low-altitude unmanned aerial vehicle (UAV) images and deep learning techniques. First, the location of the kiwifruit plants and vine distribution are extracted from high-precision images collected by UAV. The canopy gradient distribution maps with different noise reduction and distribution effects are generated by modifying the threshold and sampling size using the resampling normalization method. The results showed that the accuracies of the vine segmentation using PSPnet, support vector machine, and random forest classification were 71.2%, 85.8%, and 75.26%, respectively. However, the segmentation image obtained using depth semantic segmentation had a higher signal-to-noise ratio and was closer to the real situation. The average intersection over union of the deep semantic segmentation was more than or equal to 80% in distribution maps, whereas, in traditional machine learning, the average intersection was between 20% and 60%. This indicates the proposed model can quickly extract the vine distribution and plant position, and is thus able to perform dynamic monitoring of orchards to provide real-time operation guidance.

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A dual neural network for object detection in UAV images

Neurocomputing ◽

10.1016/j.neucom.2021.03.016 ◽

2021 ◽

Vol 443 ◽

pp. 292-301

Author(s):

Gangyi Tian ◽

Jianran Liu ◽

Wenyuan Yang

Keyword(s):

Neural Network ◽

Object Detection ◽

Uav Images

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Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

Remote Sensing ◽

10.3390/rs13071238 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1238

Author(s):

Jere Kaivosoja ◽

Juho Hautsalo ◽

Jaakko Heikkinen ◽

Lea Hiltunen ◽

Pentti Ruuttunen ◽

...

Keyword(s):

Reference Data ◽

Measurement Techniques ◽

Synthetic Data ◽

Ground Truth ◽

Precision Farming ◽

Aerial Imaging ◽

Aerial Vehicle ◽

Uav Images ◽

Reference Measurements

The development of UAV (unmanned aerial vehicle) imaging technologies for precision farming applications is rapid, and new studies are published frequently. In cases where measurements are based on aerial imaging, there is the need to have ground truth or reference data in order to develop reliable applications. However, in several precision farming use cases such as pests, weeds, and diseases detection, the reference data can be subjective or relatively difficult to capture. Furthermore, the collection of reference data is usually laborious and time consuming. It also appears that it is difficult to develop generalisable solutions for these areas. This review studies previous research related to pests, weeds, and diseases detection and mapping using UAV imaging in the precision farming context, underpinning the applied reference measurement techniques. The majority of the reviewed studies utilised subjective visual observations of UAV images, and only a few applied in situ measurements. The conclusion of the review is that there is a lack of quantitative and repeatable reference data measurement solutions in the areas of mapping pests, weeds, and diseases. In addition, the results that the studies present should be reflected in the applied references. An option in the future approach could be the use of synthetic data as reference.

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Multiscale Object Detection from Drone Imagery Using Ensemble Transfer Learning

Drones ◽

10.3390/drones5030066 ◽

2021 ◽

Vol 5 (3) ◽

pp. 66

Author(s):

Rahee Walambe ◽

Aboli Marathe ◽

Ketan Kotecha

Keyword(s):

Object Detection ◽

Transfer Learning ◽

Data Augmentation ◽

Test Time ◽

Complex Task ◽

Open Domain ◽

End User ◽

Aerial Vehicle ◽

Uav Images ◽

Voting Strategy

Object detection in uncrewed aerial vehicle (UAV) images has been a longstanding challenge in the field of computer vision. Specifically, object detection in drone images is a complex task due to objects of various scales such as humans, buildings, water bodies, and hills. In this paper, we present an implementation of ensemble transfer learning to enhance the performance of the base models for multiscale object detection in drone imagery. Combined with a test-time augmentation pipeline, the algorithm combines different models and applies voting strategies to detect objects of various scales in UAV images. The data augmentation also presents a solution to the deficiency of drone image datasets. We experimented with two specific datasets in the open domain: the VisDrone dataset and the AU-AIR Dataset. Our approach is more practical and efficient due to the use of transfer learning and two-level voting strategy ensemble instead of training custom models on entire datasets. The experimentation shows significant improvement in the mAP for both VisDrone and AU-AIR datasets by employing the ensemble transfer learning method. Furthermore, the utilization of voting strategies further increases the 3reliability of the ensemble as the end-user can select and trace the effects of the mechanism for bounding box predictions.

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EasyIDP: A Python Package for Intermediate Data Processing in UAV-Based Plant Phenotyping

Remote Sensing ◽

10.3390/rs13132622 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2622

Author(s):

Haozhou Wang ◽

Yulin Duan ◽

Yun Shi ◽

Yoichiro Kato ◽

Seish Ninomiya ◽

...

Keyword(s):

Data Processing ◽

Plant Phenotyping ◽

Phenotypic Traits ◽

Data Annotation ◽

Intermediate Data ◽

Reconstruction Quality ◽

Open Source Software Package ◽

Machine Learning Applications ◽

Data Processor ◽

Uav Images

Unmanned aerial vehicle (UAV) and structure from motion (SfM) photogrammetry techniques are widely used for field-based, high-throughput plant phenotyping nowadays, but some of the intermediate processes throughout the workflow remain manual. For example, geographic information system (GIS) software is used to manually assess the 2D/3D field reconstruction quality and cropping region of interests (ROIs) from the whole field. In addition, extracting phenotypic traits from raw UAV images is more competitive than directly from the digital orthomosaic (DOM). Currently, no easy-to-use tools are available to implement previous tasks for commonly used commercial SfM software, such as Pix4D and Agisoft Metashape. Hence, an open source software package called easy intermediate data processor (EasyIDP; MIT license) was developed to decrease the workload in intermediate data processing mentioned above. The functions of the proposed package include 1) an ROI cropping module, assisting in reconstruction quality assessment and cropping ROIs from the whole field, and 2) an ROI reversing module, projecting ROIs to relative raw images. The result showed that both cropping and reversing modules work as expected. Moreover, the effects of ROI height selection and reversed ROI position on raw images to reverse calculation were discussed. This tool shows great potential for decreasing workload in data annotation for machine learning applications.

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Urban tree analysis using unmanned aerial vehicle (uav) images and object-based classification (case study: university of indonesia campus)

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/683/1/012105 ◽

2021 ◽

Vol 683 (1) ◽

pp. 012105

Author(s):

A Wicaksono ◽

R Hernina

Keyword(s):

Unmanned Aerial Vehicle ◽

Tree Analysis ◽

Urban Tree ◽

Object Based ◽

Aerial Vehicle ◽

Uav Images

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RSNet: Rail semantic segmentation network for extracting aerial railroad images

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210349 ◽

2021 ◽

pp. 1-18

Author(s):

R.S. Rampriya ◽

Sabarinathan ◽

R. Suganya

Keyword(s):

Real Time ◽

Visual Processing ◽

Feature Fusion ◽

Semantic Segmentation ◽

Vital Role ◽

Obstacle Detection ◽

Aerial Images ◽

Computationally Efficient ◽

Fusion Algorithm ◽

Uav Images

In the near future, combo of UAV (Unmanned Aerial Vehicle) and computer vision will play a vital role in monitoring the condition of the railroad periodically to ensure passenger safety. The most significant module involved in railroad visual processing is obstacle detection, in which caution is obstacle fallen near track gage inside or outside. This leads to the importance of detecting and segment the railroad as three key regions, such as gage inside, rails, and background. Traditional railroad segmentation methods depend on either manual feature selection or expensive dedicated devices such as Lidar, which is typically less reliable in railroad semantic segmentation. Also, cameras mounted on moving vehicles like a drone can produce high-resolution images, so segmenting precise pixel information from those aerial images has been challenging due to the railroad surroundings chaos. RSNet is a multi-level feature fusion algorithm for segmenting railroad aerial images captured by UAV and proposes an attention-based efficient convolutional encoder for feature extraction, which is robust and computationally efficient and modified residual decoder for segmentation which considers only essential features and produces less overhead with higher performance even in real-time railroad drone imagery. The network is trained and tested on a railroad scenic view segmentation dataset (RSSD), which we have built from real-time UAV images and achieves 0.973 dice coefficient and 0.94 jaccard on test data that exhibits better results compared to the existing approaches like a residual unit and residual squeeze net.

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