Human Factors While Using Head-Up-Display in Low Visibility Flying Conditions

Flying an aircraft in low visibility is still a challenging task for the pilot.It requires precise and accurate situational awareness(SA)in real-time.A Head-up Display(HUD)is used to project collimated internal and externalflight information on a transparent screen in the pilot’s forwardfield of view,which eliminates the change of eye position between Head-Down-Display(HDD)instru-ments and outer view through the windshield.Implementation of HUD increases the SA and reduces the workload for the pilot.But to provide a betterflying capability for the pilot,projecting extensive information on HUD causes human factor issues that reduce pilot performance and lead to accidents in low visibility conditions.The literature shows that human error is the leading cause of more than 70%of aviation accidents.In this study,the ability of the pilot able to read background and symbology information of HUD at a different level of back-ground seen complexity,such as symbology brightness,transition time,amount of Symbology,size etc.,in low visibility conditions is discussed.The result shows that increased complexity on the HUD causes more detection errors.

An Automatic Classification Grading of Spinach Seedlings Water Stress Based on N-MobileNetXt

To solve inefficient water stress classification of spinach seedlings under complex background,this study proposed an automatic classification method for the water stress level of spinach seedlings based on the N-MobileNetXt(NCAM+MobileNetXt)network.Firstly,this study recon-structed the Sandglass Block to effectively increase the model accuracy;secondly,this study introduced the group convolution module and a two-dimensional adaptive average pool,which can significantly compress the model parameters and enhance the model robustness separately;finally,this study innovatively proposed the Normalization-based Channel Attention Module(NCAM)to enhance the image features obviously.The experimental results showed that the classification accuracy of N-MobileNetXt model for spinach seedlings under the natural environment reached 90.35%,and the number of parameters was decreased by 66%compared with the original MobileNetXt model.The N-MobileNetXt model was superior to other net-work models such as ShuffleNet and GhostNet in terms of parameters and accuracy of identification.It can provide a theoretical basis and technical support for automatic irrigation.

Dim Moving Small Target Detection by Local and Global Variance Filtering on Temporal Profiles in Infrared Sequences

In this paper, the temporal different characteristics between the target and background pixels are used to detect dim moving targets in the slow-evolving complex background. A local and global variance filter on temporal profiles is presented that addresses the temporal characteristics of the target and background pixels to eliminate the large variation of background temporal profiles. Firstly, the temporal behaviors of different types of image pixels of practical infrared scenes are analyzed.Then, the new local and global variance filter is proposed. The baseline of the fluctuation level of background temporal profiles is obtained by using the local and global variance filter. The height of the target pulse signal is extracted by subtracting the baseline from the original temporal profiles. Finally, a new target detection criterion is designed. The proposed method is applied to detect dim and small targets in practical infrared sequence images. The experimental results show that the proposed algorithm has good detection performance for dim moving small targets in the complex background.

A Complex Background Image Registration Method Based on the Optical Flow Field Algorithm

An effective nonrigid image registrationmethod is developed based on the optical flow field(OFF)framework for the complex registration of structure images.In our method,a new force is modeled and integrated into the original optical flow equation to jointly drive the motion direction of pixels.At any point in the offset field,in addition to the force generated by the OFF model derived from local gradient information to drive the pixels in the floating image to infiltrate into the reference pixel set,a new“guiding force”derived from the global grayscale overall trend in a given neighborhood system helps the pixels to more properly spread into the corresponding reference pixel set,particularly when the gradient field of the reference image is unstable.In the experiment,a data set containing several images with complex structures was employed to validate the performance of our registration model.The test results show that our method can quickly and efficiently register complex images and is robust to noise in images.

A new approach to learning and recognizing leaf diseases from individual lesions using convolutional neural networks

Leaf disease recognition using image processing and deep learning techniques is currently a vibrant research area.Most studies have focused on recognizing diseases from images of whole leaves.This approach limits the resulting models’ability to estimate leaf disease severity or identify multiple anomalies occurring on the same leaf.Recent studies have demonstrated that classifying leaf diseases based on individual lesions greatly enhances disease recognition accuracy.In those studies,however,the lesions were laboriously cropped by hand.This study proposes a semi-automatic algorithm that facilitates the fast and efficient preparation of datasets of individual lesions and leaf image pixel maps to overcome this problem.These datasets were then used to train and test lesion classifier and semantic segmentation Convolutional Neural Network(CNN)models,respectively.We report that GoogLeNet’s disease recognition accuracy improved by more than 15%when diseases were recognized from lesion images compared to when disease recognition was done using images of whole leaves.A CNN model which performs semantic segmentation of both the leaf and lesions in one pass is also proposed in this paper.The proposed KijaniNet model achieved state-of-the-art segmentation performance in terms of mean Intersection over Union(mIoU)score of 0.8448 and 0.6257 for the leaf and lesion pixel classes,respectively.In terms of mean boundary F1 score,the KijaniNet model attained 0.8241 and 0.7855 for the two pixel classes,respectively.Lastly,a fully automatic algorithm for leaf disease recognition from individual lesions is proposed.The algorithm employs the semantic segmentation network cascaded to a GoogLeNet classifier for lesion-wise disease recognition.The proposed fully automatic algorithm outperforms competing methods in terms of its superior segmentation and classification performance despite being trained on a small dataset.

Greenness identification based on HSV decision tree

Greenness identification from crop images captured outdoors is the important step for crop growth monitoring.The commonly used methods for greenness identification are based on visible spectral-index,such as the excess green index,the excess green minus excess red index,the vegetative index,the color index of vegetation extraction,the combined index.All these visible spectral-index based methods are working on the assumption that plants display a clear high degree of greenness,and soil is the only background element.In fact,the brightness and contrast of an image coming from outdoor environments are seriously affected by the weather conditions and the capture time.The color of the plant varies from dark green to bright green.The back ground elements may contain crop straw,straw ash besides soil.These environmental factors always make the visible spectral-index based methods unable to work correctly.In this paper,an HSV decision tree based method for greenness identification from maize seedling images captured outdoors is proposed.Firstly,the image was converted from RGB color space to HSV color space to avoid influence of illumination.Secondly,most of the background pixels were removed according to their hue values compared with the ones of green plants.Thirdly,the pixels of wheat straws whose hue values were intersected with tender green leaves were eliminated subject to their hues,saturations and values.At last,thresholding was employed to get the green plants.The results indicate that the proposed method can recognize greenness pixels correctly from the crop images captured outdoors.